Life Insurance in Europe: Risk Analysis and Market Challenges [1st ed.] 9783030496548, 9783030496555

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Financial and Monetary Policy Studies 50

Marta Borda Simon Grima Ilona Kwiecień  Editors

Life Insurance in Europe Risk Analysis and Market Challenges

Financial and Monetary Policy Studies Volume 50

Series Editor Ansgar Belke, University of Duisburg-Essen, Essen, Germany

More information about this series at http://www.springer.com/series/5982

Marta Borda • Simon Grima • Ilona Kwiecień Editors

Life Insurance in Europe Risk Analysis and Market Challenges

Editors Marta Borda Department of Insurance Wroclaw University of Economics and Business Wroclaw, Poland

Simon Grima Department of Insurance University of Malta Msida, Malta

Ilona Kwiecień Department of Insurance Wroclaw University of Economics and Business Wroclaw, Poland

ISSN 0921-8580 ISSN 2197-1889 (electronic) Financial and Monetary Policy Studies ISBN 978-3-030-49654-8 ISBN 978-3-030-49655-5 (eBook) https://doi.org/10.1007/978-3-030-49655-5 © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

The European life insurance sector has faced many new challenges due to new technologies and sociocultural and demographic trends. These disruptions have changed the insurance market in several ways. New technologies applied in underwriting and claims management processes are changing life insurance business practices. Today, purchasing a life insurance product is faster and easier than ever before. On the other hand, digitalization of the life insurance sector creates new challenges because it requires new approaches to risk assessment and premium calculations. European countries have also faced demographic trends, in particular population ageing. Nowadays, thanks to progress in medical and diagnostic technologies and infrastructure, improvement in the population’s health status as a result of healthy lifestyles, and changes in the educational structure of the population (including an increase in social awareness), many people are living longer and longer. However, it should be emphasized that a longer life expectancy means a longer retirement period, an increase in the financing needs of retired people, and a greater demand for health care services provided to the elderly. By offering innovative life and health insurance products, the insurance sector can complement pension and social insurance systems in managing the longevity risk. New trends in abuse and mis-selling have also arisen, triggering the need and search for different approaches to supervision and regulatory measures. The financial market crisis has already significantly affected transformations in the area of supervision; this implies a need for discussion about and response to systemic connections, on the one hand, and the growing number of regulations which significantly affects the functioning of insurance companies and compliance risk, on the other. Recently, however, there have been conflicting tendencies in the sector, such as movements to strengthen consumer protection, the large-scale phenomenon of claims based on mis-selling, and a crisis of confidence in investment insurance instruments. These issues, combined with the risk of market overregulation and the innovations in products and services, including the conditions of digitization and new risk categories, seem to be of key importance to the sector at present. v

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The objective of this book is to bring together practical, theoretical, and applied research in all areas related to life insurance products and markets in the face of these new challenges. In fact, in their individual chapters the authors deal with problems of risk analysis and evaluation in life insurance, demographic challenges in life insurance, consumer protection in the European life insurance sector, distribution of life insurance products, mortality risk modelling, application of life insurance in contemporary pension systems, and the financial stability and solvency of life insurers. The book is divided into two parts. The first part of the book consists of seven chapters and gives an overview of the European life insurance market and the challenges being faced. The first chapter deals with the analysis of risk and market challenges in the European life insurance industry. The authors depict the life insurance market position, structure, size, main features, and the most important factors, as well as its potential and development prospects. In doing this, the authors bring out the new risks, challenges for life insurance business, and regulatory changes. In Chap. 2, the author highlights European life insurance social determinants. She explains that, similar to state social insurance, life insurance mitigates the social risks of ageing, unemployment, reduced health, poverty, and simultaneously saving for future well-being. She assesses the relationship between life insurance premiums and social indicators: specifically, demographic, labour, and social protection in 24 countries of the European Union in 2007–2017. The authors of the third chapter discuss the challenges faced by life insurance companies in the Baltic states. They discuss the trends considered in these three small EU states between 2014 and 2018 and uncover the challenges that life insurers in these countries are currently facing. An evaluation of the current and future challenges in the Turkish life insurance market is carried out by the authors of the fourth chapter. The authors determine and discuss the development trends of the life insurance sector between 2008 and 2019 in Turkey. Chapter 5 relates to the role played by the European Insurance and Occupational Pensions Authority (EIOPA) in developments in the insurance sector by looking at the ‘European consumer protection model’. The authors set the global framework, then concentrate on the analysis of consumer protection as part of the global agenda, and finally focus attention on the role and performance of EIOPA in the protection of consumers in the insurance sector. In Chap. 6, the author discusses a new model of investment life insurance distribution in the context of an EU consumer protection policy. The author highlights issues related to the latest phenomena occurring in the European life insurance market which have reinforced some corrective and preventive measures aimed at the clients of investment insurance products, such as unlawful business practices of insurance companies, providing insufficient information policies, and offering products with an unclear structure. The author of the final chapter of the first part of this book (Chap. 7) examines the first years of Solvency II implementation in Poland by delving into aspects of solvency capital requirements driven by standard formula. The second part of the book focuses on innovations and risk analysis in life insurance. In the eighth chapter, problems related to longevity risk in annuity products are discussed. The authors examine annuities in which the benefits are linked to the mortality experience as an alternative to traditional annuity products.

Preface

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They investigate the business value, measuring it as the present value of future profits net of the cost of capital. The time profile of business value is also addressed. Modelling of mortality rates for elderly people for the needs of life insurance companies and pension systems is carried out by the authors of the ninth chapter. Results of their estimations of Lee–Carter model for selected European countries and simulated term annuities for the retirement schemes of elderly people in these countries are presented. Chapter 10 provides a discussion on the development of the underwriting process and related techniques, focusing on two important current trends: (1) demographic changes with longevity risk and (2) digitalization and new technologies which create extraordinary possibilities for obtaining and analysing data. The authors of the eleventh chapter undertake an interesting research issue aimed at assessing the innovativeness of the life insurance market, including processes and products, referring to reverse Barras’ cycle and Pearson’s types of innovations. This is followed by an analysis of the dilemma regarding changes in the design of life products which are expected in the implementation of product oversight and governance (POG) and may lead to primary product innovation or a mere adaptation to the new regulatory framework. Chapter 12 deals with the subject of the Internet of things (IoT) in insurance and addresses current issues, opportunities, and challenges in the areas of life and health insurance, focusing on application of wearables, health mobile apps, and medical devices. The development of current trends is pictured and analysed for the market in terms of new technologies and the application of IoT solutions by insurers in their activities, taking into account the impact on key elements of the value chain for insurance activity. Finally, Chap. 13 addresses the problem of the cancer risk as a risk which is currently increasing. The study touches on issues around the scope of the offerings of life and health insurance markets, risk reduction opportunities, and solutions in which protection is available. As the editors we would like to thank the reviewers of the book—Dr Patrick Ring and Prof. Eleftherios Thalassinos—for their commitment to review the entire study and many valuable comments which inspired this publication as well as further development directions of the research presented in the book. Finally, we hope that this book will contribute to the discussion about the condition and development of the life insurance market in Europe and its current and future challenges and provide recommendations to insurers and other financial institutions on how to manage the risk in life insurance more and more efficiently. We also trust that it will be an interesting collection for academics and researchers seeking to analyse the subject of life insurance. Wroclaw, Poland Msida, Malta Wroclaw, Poland

Marta Borda Simon Grima Ilona Kwiecień

Contents

Part I 1

Market Picture and Development Challenges

European Life Insurance Market: Analysis of Current Situation and Development Prospects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marta Borda, Magdalena Chmielowiec-Lewczuk, and Ilona Kwiecień

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2

Social Determinants of Life Insurance in the European Union . . . . Natalia Grishchenko

3

The Challenges Faced by Life Insurance Companies in the Baltic States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ramona Rupeika-Apoga, Inna Romānova, and Simon Grima

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The Turkish Life Insurance Market: An Evaluation of the Current Situation and Future Challenges . . . . . . . . . . . . . . . . . . . . Ercan Ozen and Simon Grima

45

The Role Played by EIOPA in the Developments in the Insurance Sector European Consumer Protection Model . . . . . . . . . . . . . . . . . Jan Monkiewicz and Marek Monkiewicz

59

A New Model of Investment Life Insurance Distribution in the Context of Consumer Protection EU Policy . . . . . . . . . . . . . . . . . . . Anna Ostrowska-Dankiewicz

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Analysis of Capital Requirements in Life Insurance Sector Under Solvency II Regime: Evidence from Poland . . . . . . . . . . . . . . . . . . . Dorota Jaśkiewicz

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5

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7

Part II 8

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Innovations and Risk Analysis

Longevity-Linked Annuities: How to Preserve Value Creation Against Longevity Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 Annamaria Olivieri and Ermanno Pitacco ix

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Modelling the Life Expectancy of Elderly People for Life Insurance and Pension Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 Anna Jędrzychowska and Jan Gogola

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The Challenges for Life Insurance Underwriting Caused by Changes in Demography and Digitalisation . . . . . . . . . . . . . . . . . . . 147 Ilona Kwiecień, Patrycja Kowalczyk-Rólczyńska, and Michał Popielas

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Innovation in Life Insurance: The Economic Landscape and the Insurance Distribution Directive . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 Adam Śliwiński and Pierpaolo Marano

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Internet of Things (IoT): Considerations for Life Insurers . . . . . . . 177 Aleksandra Małek

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Discussion of Reducing the Risk of Cancer in Life and Health Insurance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 Maria Węgrzyn

Part I

Market Picture and Development Challenges

Chapter 1

European Life Insurance Market: Analysis of Current Situation and Development Prospects Marta Borda, Magdalena Chmielowiec-Lewczuk, and Ilona Kwiecień

1.1

Introduction

The specific nature of life insurance results directly from the risk that is insured. In life insurance products, the main insurance risk is the risk of death and/or reaching a certain age. Consequently, these are events related to the so-called life cycle, the realisation of which causes specific financial needs of the insured or his/her relatives. Moreover, the long-term nature of life insurance facilitates the accumulation of savings that can be used in the future, for example, as an additional source of income in old age. The life insurance business has a positive impact on the economic development of the country, and vice versa. The development of the life insurance market, being part of the financial system in a given country, depends on a number of political, institutional, legal, macroeconomic, demographic, social and cultural factors (see, e.g. Burić et al. 2017; Chang and Lee 2012; Dragos and Dragos 2013; Dragos 2014; Guerineau and Sawadogo 2015; Hwang and Greenford 2005; Outreville 1996; Zietz 2003). The most important factors are (a) scope and principles of functioning of the public pension and health-care financing systems; (b) legal regulations within the insurance sector and general political and legal framework (including civil liberties, political and legal stability with property and contract rights and fiscal freedom); (c) macroeconomic conditions (real interest rates, GDP, inflation and unemployment); (d) demographic and social factors (life expectancy at birth, mortality rates and health status, including self-perceived and future health risk); (e) financial market performance; and (f) insurance knowledge and awareness in a society. Life insurance also affects the socio-economic development of countries.

M. Borda (*) · M. Chmielowiec-Lewczuk · I. Kwiecień Wroclaw University of Economics and Business, Wrocław, Poland e-mail: [email protected]; [email protected]; ilona. [email protected] © Springer Nature Switzerland AG 2020 M. Borda et al. (eds.), Life Insurance in Europe, Financial and Monetary Policy Studies 50, https://doi.org/10.1007/978-3-030-49655-5_1

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It is important to emphasise that life insurers play a dual role as providers of protection and long-term savings products and as long-term stable institutional investors underpinning the economy. The life insurance companies provide financial resources to the market for entities that demand them and thus affect the economic development of the country and contribute to the accumulation of savings made by households. Historically, Europe has been played an important role in the development of the life insurance sector. The first life tables were compiled by astronomer Edmund Halley in 1693, based on the record books of the city of Wroclaw. It is stated that the first life insurance policy was issued in the eighteenth century by the Amicable Society for a Perpetual Assurance Office, founded in London in 1706 by William Talbot and Sir Thomas Allen. The concept itself, however, dates back to ancient Greece (Prudential 1915). Today, the European life insurance market is not the largest in the world rankings, although its position is still important. At the same time, many phenomena perceived in the scientific researches and reports as important for development of the life insurance business in general occur in Europe. They include changes in demography, customer behaviours, institutional and legal framework and the macroeconomic environment, as well as in technology and business models. The purpose of this chapter is to analyse and discuss the current position of the European life insurance market, as well as its potential and developmental prospects. In the next section, the attention is turned to the position of Europe as a region against the backdrop of the world. Subsequently, in the third part of the chapter, the main market measures based on the value of gross written premiums and paid benefits are examined in order to indicate new trends or to confirm existing trends. The importance of macroeconomic factors in the development of the European life insurance sector is considered in part 4, and finally, in the fifth section, the new risks and challenges for life insurance business are discussed.

1.2

European Life Insurance Market Against the Backdrop of the World

Based on the data presented in the Swiss Re Institute report on the global insurance market, in 2018 (Swiss Re 2019), global life insurance direct premium reached USD 2820 billion, constituting 54% of global insurance premium volume. These values mean that there was growth by only 0.2% in real terms from 2017 in life premiums; in non-life, the growth was more significant, at 3.0%. It was also below the annual average of the previous 10 years (0.6%), but according to the Swiss Re Institution, the increase in annual world growth in 2019/2020 is expected to be up to 3%, mostly in emerging markets, including emerging Europe. Europe is the second-largest regional market, generating in 2018 at least 33.17% of world life insurance premiums, compared to the Asia-Pacific region, which is the

1 European Life Insurance Market: Analysis of Current Situation and Development. . .

5

biggest market, with a 38.74% share of world life premiums (23% advanced and 15.7% emerging countries). North America had a 22.96% share. The majority of the premiums is generated by countries included in Advanced EMEA (Europe, Middle East and Africa), although Israel is the only non-European country in this Swiss Re group. This group consists of 20 countries in Western Europe. The share of life insurance premiums of these Advanced Europe countries in 2018 constituted 32.76% (see Table 1.1). Nine other European countries, in Central and Eastern Europe, contribute a 0.41% share in world life insurance premium volume. Non-listed countries together account for less than 0.01% of the share. The share of the world life insurance market of the Eurozone countries was 20.99%. This illustrates the clear, vast gap in terms of the potential in the global market between Western Europe and Central and East part. However, if we compare the annual average wages to the density ratio (life insurance premium per capita) in analysed European countries, we find that some emerging markets, such as Slovenia, Slovakia, Poland and the Czech Republic with the annual average wages amounts close to Portugal, Greece, Malta and Cyprus, spend much less for life insurance (per capita). It is worth noting that EU-Central and Eastern Europe countries are assessed as having an important impact on the slowdown in the Emerging Europe and Central Asia group, due to “disappointing performance” (Swiss Re 2019). The growth in life premium decreased to 3.4% in the region in 2018, down from 13% in the previous year. The strongest decrease is noted in Poland, where the premiums written fell by 11%, and in the Czech Republic, they fell by 3.9%. In fact, in Poland, according to annual reports published by the Polish Financial Supervision Authority, the continuous decrease in premium written in life insurance has been observed since 2012, which is significant due to the fact that it is the largest market in terms of premium written in the region (www.knf.gov.pl/en). This huge decrease in 2018 in Poland is related to the group of linked to capital funds insurance—while the premium written in this group felt by 30% from 2017, which is considered among others as the market response to mis-selling cases, including class actions against insurance companies. The investment-insurance-linked products have been identified in the Polish Financial Ombudsman report as essentially not giving customers a chance to earn, while the level of their complexity was so high that they should not be offered to the average consumer, and possible profits were absorbed by numerous fees. When analysing the potential of European countries on the global market, it should be noted that among the top five countries in the world in terms of participation in world life insurance premium volume, there are two countries from Europe: the United Kingdom and France (Table 1.2). Their share is expected to decrease in the forecast for the period through to 2029, as well as the global position of Europe (as the part of the advanced EMEA), in favour of China (Swiss Re 2019). However, if we analyse life insurance premiums per capita, the top five in the world ranking are ordered differently. Then it includes three European countries— but other than using the criterion of participation in the premium: (1) Hong Kong, with USD 8204 life insurance premium per capita; (2) Denmark, with USD 4590; (3) Ireland, with USD 4356; (4) Taiwan, with USD 4320; and (5) Finland, with USD

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Table 1.1 The share of European countries in world life insurance market with density ratio and wages data in 2018

Advanced

Emerging

Country United Kingdom France Italy Germany Ireland Spain Switzerland Luxembourg Sweden Denmark Finland Belgium Netherlands Norway Portugal Austria Liechtenstein Greece Malta Cyprus TOTAL (average) advanced Poland Czech Republic Hungary Slovakia Slovenia Romania Serbia Bulgaria Ukraine TOTAL (average) emerging

Share in world life insurance premium in % 8.35

Life insurance premium per capita in USD 3532

Annual average wage in USD 44,770

5.85 4.44 3.42 2.25 1.21 1.08 1 0.96 0.94 0.79 0.66 0.56 0.43 0.35 0.23 0.08

2370 2110 1161 4356 732 3555 2995 2653 4590 4019 1623 913 2276 934 746 614

0.08 0.06 0.02 32.76

207 916 500 1978

44,510 37,752 49,813 47,303 38,761 64,109 65,449 44,196 55,253 44,111 52,080 54,262 50,956 25,487 50,868 116,430 (2015) 26,671 22,572 2137 (2017) 46,874

0.15 0.09

115 244

29,109 26,962

0.06 0.03 0.03 0.02 0.01 0.01 0.01 0.41

180 180 409 26 32 29 3 115

24,455 25,357 37,322 11,290 (2015) 6390 (2015) 8860 (2015) 2660 (2015) 19,156

Source: own work based on Swiss Re (2019), OECD (2018), UNECE (2017) stats and wolrddata. info (data marked as 2015)

1 European Life Insurance Market: Analysis of Current Situation and Development. . .

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Table 1.2 Top five countries in the world life insurance sector

Country US Japan China United Kingdom France

Premium volume in millions of USD 593,391 334,243 313,365

Inflation adjusted change in % (2018) 2.4 3.6
5.4

Life premium in % of GDP 2.88 6.72 2.30

Life premium per capita in USD (2018) 1810 2629 221

8.35

235,501


1.6

8.32

3532

Average annual wages in USD (2018) 63,093 40,573 9470 (2015) 44,770

5.85

165,075


1.4

5.75

2370

44,510

Share in world life premium in % (2018) 21.04 11.85 11.11

Source: own work based on OECD (2018) and UNECE (2017) data and https://www.ceicdata.com/ en/indicator/china/monthly-earnings

4019 Swiss Re (2019). To compare, the world average life insurance premium per capita in 2018 was USD 370; in advanced Europe (advanced EMEA), it was USD 1978; and the average per capita in emerging European was only USD 115 (Table 1.1). Against the background of the global insurance market, the Swiss Re (2019) analysis shows that low interest rates continue to depress profitability in life insurance, which is especially observed in Europe, as well as in advanced Asia-Pacific. The shareholder-equity weighted average ROE for a sample of 18 European insurers declined from 2017 by 1.0 ppt to 8.5% in 2018, in comparison to an improvement by 2.4 ppt value to 11.3% for composite and life 23 examined insurers from US market and 10.4% (9.4 in 2017) among 32 Asia-Pacific insurers.

1.3

Analysis of the Life Insurance Sector in Europe Since 2009

The development of the life insurance market is best characterised by two factors: premiums and benefits. Both these positions allow us to evaluate the dynamics of the development of the life insurance market, as well as supply information on the sales volumes in the insurance sector (premiums) and on the costs borne by the insurers (benefits). Both premiums and benefits are also basic factors shaping the financial results of the insurance company. Figure 1.1 presents premiums collected in the life insurance sector on the European market in the years 2009–2018. As indicated above, the European life insurance market in 2018 constituted 33% of the global market, as far as gross written premium is concerned. Over the course of the years 2009–2018, the premium in question revealed a growing trend. In the year 2018, it amounted to 764 billion (i.e. 764,000,000,000) euro, which means a

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€bn 800

8.0% 6.0%

700

4.0% 600

2.0%

500

0.0%

-2.0%

400

-4.0% 300

-6.0%

200

-8.0% 2009

2010

2011

2012

2013 2014 2015 2016 Premiums Growth rates

2017

2018

Fig. 1.1 Life premiums (in Euros, bn). Source: https://www.insuranceeurope.eu/insurancedata (access 7.10.2019) €bn 160 000 140 000 120 000 100 000 80 000 60 000 40 000 20 000

UK FR IT DE ES CH SE DK FI BE NL IE NO PT AT PL GR LU HU TR SK SI RO HR MT CY BG EE LV 2017

2018

Fig. 1.2 Life premiums by country (2017–2018). Source: https://www.insuranceeurope.eu/ insurancedata (access 7.10.2019)

6.7% growth against the previous year. However, despite the general upward trend of the premium, there were years when a fall in its value was recorded. Such a situation occurred, for instance, in 2011 and 2012, which was, most probably, the aftermath of an earlier financial crisis. Another significant fall in the value of the premium took place in 2016. Its cause is commonly ascribed to the decrease in the sales of the unit-linked insurance, which—in turn—was brought about by a poor economic situation in the financial markets. The breakdown of the premium collected in the life insurance sector in separate countries is presented in Fig. 1.2. The countries where the largest premium was

1 European Life Insurance Market: Analysis of Current Situation and Development. . .

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€bn 700

14.0% 12.0% 10.0%

600

8.0%

500

6.0% 4.0% 2.0%

400

0.0% -2.0%

300

-4.0% 200

-6.0% 2009

2010

2011

2012

2013

Benefits paid

2014

2015

2016

2017

2018

Growth rates

Fig. 1.3 Life benefits (in Euros, bn). Source: https://www.insuranceeurope.eu/insurancedata (access 7.10.2019)

collected in the life insurance sector include the United Kingdom, France, Italy and Germany. The total sum of the premium collected in these countries in the years 2017 and 2018 rose from 90 to almost 200 billion euro. The second group of countries is comprised of countries in which the premium collected ranges from 10 to 30 billion euro (e.g. Spain, Switzerland, Sweden, Denmark). In the third group, the premium was at the level of 1–6.5 billion euro. This group contains such countries as Portugal (the highest level) and Slovakia (the lowest level). In the remaining countries, the premium collected amounted to less than 6 billion euro. Figure 1.3 presents the sums paid out in benefits by insurance companies in the European market over the period of 2009 to 2018. The volume of benefits paid out in the life insurance sector presented itself differently from the volume of the premiums collected in the same period of time. Admittedly, the volume of the benefits paid out revealed a growing trend, too, but there were also years which experienced a fall: 2013 and 2016. Those years, however, do not overlap with the ones when there was the fall in the premiums (Fig. 1.1). The cause nevertheless seems to be the same: the worse economic situation in the financial markets. An exception, however, is that in the case of benefits, the aftereffects in the form of reactions to financial market turbulence are always delayed. Additionally, the fall in paid-out benefits is also conditioned by the effect of other factors (e.g. ones connected with the internal situation of individual countries). Figure 1.4 presents the volumes of benefits paid out in the life insurance sector in individual European countries. Comparing the data set out in Fig. 1.4 with those presented in Fig. 1.2, we can easily notice that the order of particular countries resulting from the volume of benefits which they paid out is slightly different from that illustrating the volume of

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€bn 180 000 160 000 140 000 120 000 100 000 80 000 60 000 40 000 20 000

UK FR DE IT CH ES SE NL FI DK BE IE AT PT NO PL CZ GR LU SK SI TR HR MT RO CY 2017

2018

Fig. 1.4 Life benefits by country (2017–2018). Source: https://www.insuranceeurope.eu/ insurancedata (access 7.10.2019)

the premiums collected. The same holds true for all four groups of countries discussed with regard to premiums—both at the top and at the bottom of the table. In 2017 on the European life insurance sector, the dominant group of products was the sale of individual contracts, which amounted to 80% of the market. The remaining 20% was comprised of group contracts. In terms of division into traditional and unit-linked life insurance contracts, about 75% of the policies sold by life insurers in 2017 were non-unit-linked products, whereas the remaining 25% was comprised of unit-linked contracts. Even though this structure did not differ significantly in particular European countries, there were also exceptions in this respect. The country in which unit-linked contracts accounted for 60% of the market was Hungary. In the last few years, unit-linked products have outperformed guaranteed products in terms of premium growth, but they are still smaller in volume. In general, this shift was driven by one or both of the following reasons [https://www. insuranceeurope.eu/insurancedata (access 7.10.2019)]: • Low interest rates, which make guaranteed products less attractive • The increased cost of capital for guaranteed products as a result of their treatment under the EU’s Solvency II regulatory regime Figure 1.5 presents the share measured with the premium in the three largest European countries on the market. In the European life insurance market, the dominant position is occupied by four countries which generate the highest premiums. These include the United Kingdom, with the largest market share of 28%, which assures it the position of an unchallenged leader; France, with 19% of the market share; Italy, with 14%; and,

1 European Life Insurance Market: Analysis of Current Situation and Development. . .

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Market structure

26%

28%

United Kingdom France Italy Germany

13% 14%

19 %

Others

Fig. 1.5 Market structure by country in Europe. Source: https://www.insuranceeurope.eu/ insurancedata (access 25.10.2019)

finally, Germany, with 13%. All four states are large European countries, which of course has led to the market concentration we witness today. However, the United Kingdom, with its long and rich insurance tradition, stands out against this background, as its insurance market is one of the oldest in Europe, which translates into decisively greater insurance awareness of its citizens.

1.4

Macroeconomic Factors in European Life Insurance Sector Development

As mentioned above, macroeconomic conditions affect the life insurance industry. However, the strength of the impact of specific economic factors on the life insurance market depends on the country. Among the most important economic indicators that can be used to describe the relation between economic environment and life insurance industry development are the following: GDP; inflation rate; unemployment rate; interest rates; average wage; savings rate; and available income per capita. The relationship between economic conditions and the life insurance market has been examined by many researchers, who have analysed the link between basic economic indicators, such as GDP, inflation rate, interest rates, unemployment rate, available income and the amount of life insurance premiums. The results of the studies vary due to the countries included in the analysis. Below, a short review of the selected research findings in this field is presented. With regard to Asian countries, Sen (2008) proved that there is a significant positive relation between life insurance premiums and gross domestic savings and income per capita, while inflation has a negative impact on the life insurance market. Based on an

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12.00% DK 10.00%

FI FR

8.00%

HU IT

6.00%

LV

4.00%

NO PL

2.00%

SK UK

0.00% 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Fig. 1.6 Insurance penetration in selected European countries in 2008–2018. Source: own work based on data from (Insurance Europe 2019)

examination of 30 Organisation for Economic Co-operation and Development (OECD) countries in 1993–2000, Li et al. (2007) showed that there was a positive correlation between life insurance demand and income, the level of education and financial development of the country; however, inflation and interest rates were negatively correlated with life insurance premiums. Dragos (2014) found that income has a positive impact on life insurance demand in CEE countries; however, it has no significant impact on developing Asian countries. Bianchi et al. (2011) found that potential growth of the insurance market in Central, Eastern and South Eastern Europe is closely related to the economic growth in this region. Burić et al. (2017), when analysing Western Balkan countries, came to conclusion that GDP and wages have a significant positive impact on life insurance development, while the impact of unemployment and interest rates is negative. We now turn our focus to economic growth as a factor affecting life insurance development in European countries. In the first step, the insurance penetration ratio is examined. This ratio belongs to so-called basic insurance market development indicators, and it is defined as the relation of gross direct written premiums to GDP (Insurance Europe, 2019). Figure 1.6 presents the penetration ratio calculated for the selected European countries in the years 2008–2018. According to Insurance Europe (2019), in 2018, the total insurance premium collected by European life insurers constituted on average 4.35% of GDP, and the penetration ratio has shown a slight downward trend over the past decade (a decrease of 0.46 p.p. compared to the value noted in 2009). Among the European countries, the differences in level of development of life insurance markets measured with the penetration ratio are significant (Fig. 1.6). One can observe that the countries with developed life insurance markets and relatively high levels of household income are usually characterised by the high share of premium in GDP. In 2018, the highest

1 European Life Insurance Market: Analysis of Current Situation and Development. . .

13

Table 1.3 Correlation between life insurance premiums and GDP in European countries Country AT BE BG CH CY DE DK ES FI GR

Correlation coefficient
0.8631
0.7724 0.7602 0.8858 0.6168 0.6576 0.9886 0.6041 0.8130 0.9631

Country HR IE LU LV MT NL NO PL UK

Correlation coefficient 0.7710 0.8283 0.6993 0.7173 0.9568
0.8771 0.8132
0.7279 0.7612

Note: all correlation coefficients are statistically significant at level 0.05 Source: own calculations based on data from Insurance Europe Database

penetration ratio was recorded in the United Kingdom (9.6%), followed by Finland (8.21%), Denmark (7.74%) and France (5.94%), while the lowest value of this ratio was noted in Latvia (0.16%), Romania (0.21%), Estonia and Bulgaria (0.32%) (Insurance Europe Database 2019). The insurance penetration in CEE countries has remained at a substantially low level, which results not only from the relatively low amounts of collected life premiums but also from the fact that in the examined period, the growth of GDP was higher than the growth of premiums collected in the life insurance sector. In some countries (e.g. Poland), an increase in GDP was accompanied by a decrease in life insurance premiums. In order to examine the relationship between life insurance premiums and GDP, Pearson’s correlation coefficient was applied, which is a particular case of the general correlation coefficient (Bernstein and Bernstein 1999; Sharma 2007). Pearson’s correlation coefficient examines the linear relationship between the variables, and it takes values from the interval [
1; 1]. The closer the coefficient is to 1, the stronger the positive correlation between the analysed variables (see more in Borda and Kowalczyk-Rólczyńska 2016). We calculated Pearson’s correlation coefficient for 29 European countries with complete available data in the period of 2008–2018. Next, we conducted a significance test of the correlation coefficient using the significance level α ¼ 0.05. For 19 countries from the original set of 29 examined, Pearson’s correlation coefficient values were statistically significant. The results of this part of the study are presented in Table 1.3. As presented in Table 1.3, in the case of most analysed countries, a strong positive correlation between life insurance premiums and GDP was recorded. The correlation coefficient took the highest values for Denmark, Greece, Malta, Finland, Norway and the United Kingdom. Moreover, in Germany, Spain and Cyprus, the relationship between life insurance sector development and GDP is also positive. This result confirms the general finding that economic growth leads to the development of the life insurance sector, and vice versa. It is important to mention that in Greece, the life insurance market has shrunk by 25%, with a simultaneous decrease

14

M. Borda et al.

in GDP recorded from 2008 to 2016. A strong negative correlation was recorded in Austria, Belgium, the Netherlands and Poland. In these countries, a growth in GDP was accompanied by a simultaneous decline in life insurance premiums. In the Netherlands, the amount of gross written premiums in life insurance sector decreased by more than half over the examined period. In Poland, over the last decade, the life insurance market has shrunk by 28.3% (and in Austria, 24.5%; in Belgium, 15.5%, respectively). The largest decreases were recorded after 2009, as a result of the financial crisis, and again from 2015. This situation means that other factors, such as the European Central Bank policy of maintaining low interest rates, as well as the financial market performance, can be stronger determinants of the life insurance sector development in these countries.

1.5

New Risks and Challenges in the Life Insurance Sector

In the life insurance sector, we can distinguish two groups of new risks and the accompanying challenges which the insurance market is now facing. The first of these groups regards insurance risk, that is, the type of risk that is directly related to life insurance products. The second group deals with risks and challenges which influence the risk of the insurance company as the entity conducting the business activity. These relationships are presented in Fig. 1.7. Figure 1.7 presents the group of challenges and new risks. They can influence the insurance risk and forming life insurance products. This group includes: • Extension of human lifespan: first of all, due to progress in medicine (e.g. in recent years, for the insurance sector has grown in importance a more and more popular practice of genetic testing)

EXTENSION OF HUMAN LIFE

PERMAFROST BACTERIA/VIRUS RELEASE

LIFE INSURANCE MARKET

INSURANCE RISK

RESPIRATORY DISEASES

VIRTUEL ASSETS

CARDIOVASCULAR DISEASES

COLLECTING LIFESTYLE DATA

BUSINESS RISK

NEW TECH

INTERNET OF THINGS

COMPLIANCE

Fig. 1.7 New risks and challenges for life insurance. Source: own study based on https://www. swissre.com/institute/research/sonar/sonar2019.html; https://www.insuranceeurope.eu; https:// www.globalreinsurance.com/home/five-emerging-trends-and-15-emerging-risks-for-re/insurers-in2019/1430447.article

1 European Life Insurance Market: Analysis of Current Situation and Development. . .

15

• The risk of the appearance of new bacteria and viruses and the risk of the consequential outbreaks of epidemics and pandemics; the effect of climatic change (e.g. permafrost thawing that leads to the development of new bacteria and viruses) • An increased risk of cardiovascular diseases: a direct consequence of global warming (higher temperature and greater number of hot days per year) • An increased risk of respiratory system diseases: again, a direct consequence of changes to the climate and weather (e.g. the appearance of wildfires and the increased air temperature) Within the second group, which is concerned with business risk, one can single out the following risks and challenges for the life insurance sector: • The risk involved in the application of state-of-the-art technologies in business activities of the insurance company (e.g. application of block-chain or digitalisation systems, etc.) • The risk involved in an adequate estimate of virtual assets and their presentation in financial reports • The risk involved in obtaining data necessary to make an estimate of the insurance risk for life insurance products (design and implementation of systems monitoring and surveying lifestyles) • The risk involved in the use of the Internet (“Internet of Things”); the sale of products online, cybercrime, etc. • The risk of compliance related to changing regulations, activities of supervisory authorities, as well as pro-consumer court rulings, including the most recent significant rulings, such as the EU Directives Solvency II (2016); IFRS 17 standard (announced in 2017, to be enforced in 2022); General Protection Data Regulation (2018); Insurance Distribution Directive (2018); and, for example, court rulings challenging the insurance nature of investment policies (e.g. in Italy, sentence no. 10333/2018 the Italian Supreme Court (Corte di Cassazione) sentenced against the (ab)use of so-called “unit-linked” life insurance policies for tax avoidance purposes; 5608 of December 10, 2018, the Italian Provincial Tax Court of Milan); as well as the aforementioned class actions in Poland claiming abusive clauses about liquidation fees or lack of proper investment risk information Meeting all the challenges resulting from the impact of various factors and the capacity of risk management in new conditions will—in the near future—be the task of insurance companies concentrated in the life insurance sector. All of mentioned issues and phenomena pose additional challenges in business models and compliance chains, implying costs for insurers, mostly due to updating IT systems influencing business profitability.1

1 Insurers highlight challenges of applying GDPR; https://www.insuranceeurope.eu/insurers-high light-challenges-applying-gdpr.

16

1.6

M. Borda et al.

Conclusions

The European life insurance market is significant in terms of global market share, although it is very uneven. We are still observing a significant difference between the potential of Western Europe and Central and Eastern Europe. However, CEE’s economic development does not go hand-in-hand with a significant increase in life insurance premiums. According to data for 2018, the average annual premium per capita in European emerging markets was 17 times smaller than the average annual premium per capita in advanced markets, while the annual average wages were only 2.4 times smaller. This makes it necessary to seek out the reasons for these differences in the development processes. Most examined European countries were characterised by a strong positive correlation between life insurance premiums and GDP in 2008–2018, which confirms the general finding that economic growth leads to the development of the life insurance sector, and vice versa. However, in the case of Austria, Belgium, the Netherlands and Poland, a negative correlation was recorded in the analysed period, what indicates that despite the GDP growth, these countries experienced a simultaneous decline in life insurance premiums, determined by other factors. The life insurance market depends on many factors, and those that shape insured risk are significant. No less important, however, are customer behaviour affecting demand and also decisive tendencies in the propensity of customers to verify old contracts as well as the consumer-related case law. It can also be expected that in the coming years there may be changes related to the climate and our lifestyle that will be significant for the design of life insurance products. Insurance companies should therefore already be prepared for these changes.

References Bernstein, S., & Bernstein, R. (1999). Schaum’s outline of elements of statistics I: Descriptive statistics and probability. London: McGraw-Hill. Bianchi, T., Ebner, G., Korherr, R., & Ubl, E. (2011). The Austrian insurance industry in CESEE: Risks and opportunities from a financial stability point of view. Financial Stability Report, 22, 88–106. Available at https://www.researchgate.net/publication/227462654_The_Austrian_ Insurance_Industry_in_CESEE_Risks_and_Opportunities_from_a_Financial_Stability_Point_ of_View Borda, M., & Kowalczyk-Rólczyńska, P. (2016). Impact of demographic factors on household financial decisions – evidence from Poland. International Journal of Risk Assessment and Management, 19(1/2), 106–124. Burić, M. N., Smolović, J. C., Božović, M. L., & Filipović, A. L. (2017). Impact of economic factors on life insurance development in Western Balkan countries. Zbornik Radova Ekonomskog Fakulteta u Rijeci, 35(2), 331–352. Chang, C. H., & Lee, C. C. (2012). Non-linearity between life insurance and economic development: A revisited approach. The Geneva Risk and Insurance Review, 37(2), 223–257.

1 European Life Insurance Market: Analysis of Current Situation and Development. . .

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Dragos, S. E. (2014). Life and non-life insurance demand: The different effects of influence factors in emerging countries from Europe and Asia. Economic Research, 27(1), 169–180. https://doi. org/10.1080/1331677x.2014.952112 Dragos, S. L., & Dragos, C. M. (2013). The role of institutional factors over the national insurance demand: Theoretical approach and econometric estimations. Transylvanian Review of Administrative Sciences, 39E, 32–45. Guerineau, S., & Sawadogo, R. (2015). On the determinants of life insurance development in Sub-Saharan Africa: The role of the institutions quality in the effect of economic development (halshs-01178838). Hwang, T., & Greenford, B. (2005). A cross-section analysis of the determinants of life insurance consumption in Mainland China, Hong Kong, and Taiwan. Risk Management and Insurance Review, 8(1), 103–125. https://doi.org/10.1111/j.1540-6296.2005.00051.x Insurance Europe. (2019). European insurance industry database. https://www.insuranceeurope. eu/insurancedata Li, D., Moshirian, F., Nguyen, P., & Wee, T. (2007). The demand for life insurance in OECD countries. The Journal of Risk and Insurance, 74(3), 637–652. https://doi.org/10.1111/j.15396975.2007.00228.x OECD. (2018). Data. https://stats.oecd.org/Index.aspx?DataSetCode¼AV_AN_WAGE. CEIC data: https://www.ceicdata.com/en/indicator/china/monthly-earnings Outreville, F. J. (1996). Life insurance markets in developing countries. The Journal of Risk and Insurance, 63(2), 263–278. https://doi.org/10.2307/253745 Prudential. (1915). The documentary history of insurance, 1000 B.C. – 1875 A.D (p. 16). Newark, NJ: Prudential Insurance Company of America. https://archive.org/details/cu31924030231736/ page/n19 Sen, S. (2008). An analysis of life insurance demand determinants for selected Asian economies and India (Finance Working Papers, 22512). East Asian Bureau of Economic Research. Sharma, J. K. (2007). Business statistics. Delhi: Dorling Kindersley. Swiss Re. (2019). World insurance: The great pivot east continues. Sigma No. 3/2019. https:// www.swissre.com/dam/jcr:b8010432-3697-4a97-ad8b-6cb6c0aece33/sigma3_2019_en.pdf UNECE. (2017). https://w3.unece.org/PXWeb2015/pxweb/en/STAT/STAT__20-ME__3-MELF/ 60_en_MECCWagesY_r.px/table/tableViewLayout1/ Zietz, E. N. (2003). An examination of the demand for life insurance. Risk Management and Insurance Review, 6(2), 159–191. https://doi.org/10.1046/j.1098-1616.2003.030.x

Chapter 2

Social Determinants of Life Insurance in the European Union Natalia Grishchenko

2.1

Introduction

The main reasons for life insurance include risk avoidance and savings, the latter have become dominant in recent decades. Life insurance is more common in the insurance market of the European Union (EU) in comparison with the P&C segment (Fig. 2.1). The total European direct gross written premiums in 2017 amounted to 1213 billion euros, of which 710 billion euros, or almost 59%, were life premiums. Life insurance also showed the highest growth in 2017 at 5%, compared with P&C growth at 4.4% and health insurance at 3.9%. Along with health insurance, life insurance occupies the most significant part of the insurance market. Post-crisis 2009 showed the greatest share of life insurance premiums, which underlines the precautionary measures taken by the EU population in the face of economic uncertainty and crisis. In addition, life insurance premiums by type of contract show an increase in the number of individual contracts and unit-linked life insurance products in 2015–2016 (Table 2.1). This reliance on savings shows that life insurance is becoming more personalized and focused on maintaining well-being rather than risk protection. Life insurance with its objectives of saving, precautionary, life cycle, bequest and wealth accumulation is closely related to the goals of state social security. Nowadays, social security and social insurance are under pressure from the demographic situation: declining birth rate, aging and rising health-care costs. Future uncertainty requires the need for reserved savings. This uncertainty in a social context is associated with future instability in social protection, e.g. pensions, social payments, changes in the world of work, demographic and tax impact. In addition, longer life expectancy can constantly increase savings in response to future changes in the life

N. Grishchenko (*) National Research University Higher School of Economics, Moscow, Russia © Springer Nature Switzerland AG 2020 M. Borda et al. (eds.), Life Insurance in Europe, Financial and Monetary Policy Studies 50, https://doi.org/10.1007/978-3-030-49655-5_2

19

20

N. Grishchenko 1400 1200 1000

122

128

131

132

331

331

347

361

371

634

653

706

739

696

710

2012

2013

2014

2015

2016

2017

108

113

115

120

800

320

324

329

600 400

678

639

2010

2011

200 0

Life insurance premiums

Non-life insurance premiums

Health insurance premiums

Fig. 2.1 Total gross written insurance premiums on the insurance market in Europe, 2010–2017 (in billion euros). Source: Statista (2019)

Table 2.1 Life insurance by type of contract (in million euros) in Europe, 2015–2016 Individual Group Non-unit-linked Unit-linked

2015 364,736 97,332 371,775 103,642

2016 352,403 93,007 339,314 119,541

Insurance Data. Data is provided by Insurance Europe members (the members of the European Union (except Lithuania), Switzerland, Iceland, Norway, Liechtenstein and Turkey), https://www. insuranceeurope.eu/insurancedata Source: Insurance Data (2019)

cycle. The prevalence of life insurance and its growing dependence on saving motives led to the importance of the impact of social determinants.

2.2

Connected Literature

A significant number of studies are devoted to assessing the impact of economic factors on life insurance. Income, inflation, interest rate, GDP, unemployment and others are among the most used economic indicators for the evaluating the demand for life insurance. Celik and Kayali (2009) found that income is a central variable that positively affects life insurance consumption along with the population, while education level and inflation have a negative impact using data from 31 European countries for the period of 2000–2006. Using panel data for 90 countries for the period 2000–2008, Feyen et al. (2011) revealed that life insurance premiums depend on per capita income, population size and density, demographic structures, income

2 Social Determinants of Life Insurance in the European Union

21

distribution, the size of the public pension system, state ownership of insurance companies, the availability of private credit and religion. Evaluating the determinants of demand for life insurance in 14 countries in Central and South-Eastern Europe for the period 1998–2010, Kjosevski (2012) showed that GDP per capita, inflation, health expenditure, level of education and rule of law are the most stable predictors of life insurance, while real interest rates, ratio of quasi-money, young dependency ratio, old dependency ratio, control of corruption and government effectiveness are not related to the demand for life insurance. Burić et al. (2017) indicated that GDP and wages have a significant and positive impact on life insurance in countries of the Western Balkans in the period 2000–2015, while the effects of unemployment and interest rates are negative. Analysing the interest rates and inflation risk associated with the consumption of life insurance, Han and Hung (2017) found that this demand over the planning horizon increases with a measure of relative risk aversion, but decreases with the elasticity of intertemporal substitution. In this case, the optimal consumption depends on the insurance premium load, and the direction depends on the size of this elasticity relative to unity. In addition, some special studies were conducted on life insurance by effects of financial literacy (Lin et al. 2017), the financial crisis of 2008–2009 (Firtescu 2014) and the reverse effect of life insurance on economic growth (Hou et al. 2012). Without reducing the significant impact of economic factors on life insurance, we concentrate on its dependence on social determinants: how changes in social protection and the social situation are perceived by individuals and households. In this regard, we consider life insurance as one of the needs for future and additional social protection. Mahdzan and Victorian (2013), examining the determinants of demand for life insurance among life insurance policyholders of five major life insurance companies in Malaysia, confirmed the relationship between demographic factors, savings motives and the demand for life insurance. At the same time, financial literacy was insignificant in determining the demand for life insurance. Alhassan and Biekpe (2016) found that demographic factors better explain life insurance consumption compared with financial factors for life insurance in Africa using the example of 31 African countries from 1996 to 2010. They found that increased income, inflation, dependency ratio and life expectancy lead to lower life insurance consumption. Financial development, health expenditure and institutional quality showed a positive impact on life insurance consumption in Africa. The increasing demand for health care and long-term care services provided to senior citizens, and further initiatives to commercialize hospitals led to the additional demand for personal insurance products, as highlighted in a study of Borda et al. on the example the Polish and Russian health systems for the 2000s (2017). Shi et al. (2015) found that both the human capital protection motive and the asset allocation motive are important in explaining the purchase of life insurance in China. Arltova and Kabrt (2018), using Czech data, proved that an increase in young dependency ratio and old dependency ratio with some economic indicators had a negative impact on the demand for life insurance, while tertiary education and the size of the public sector

22

N. Grishchenko

were insignificant. Exploring the role of investment in life insurance in shaping individuals’ attitudes towards participation in stocks and mutual funds, Cavapozzi et al. (2013) confirmed that this is consistent with behavioural models in which economic agents are primarily interested in avoiding unacceptable adverse scenarios by purchasing low-risk investments, such as life insurance policies, and then investing in riskier assets, such as stocks and mutual funds, to get higher economic returns. Examining how the bequest motive affects consumption, investment, life insurance and retirement decision of a wage earner with an uncertain lifetime, Lim and Kwak (2016) found that unlike the fixed retirement time model, risky investment before retirement decreases as the bequest motive becomes stronger or as the incentive to retire is weaker. Wang et al. (2018) showed that financial crises and structural changes in the global economy affect the relationship between economic growth, the growth of insurance consumption and the growth of health expenditures: the variable of health shocks represented by perceived health and socioeconomic status has a positive dynamic effect on the above indicators, but has different effects in the long and short term, on levels with high and low income. The impact of economic uncertainty and crises also led to the dominance of a precautionary motive through in life insurance in Russia (Grishchenko 2019). Sliwinski et al. (2013) confirmed that among economic and financial factors, the share of health expenditure in GDP and the unemployment rate effect the demand for life insurance in Poland. There were studies on the demand for life insurance with the impact of household life cycle (Liebenberg et al. 2012), culture (Outreville 2018) and sub-regional differences (Millo and Carmeci 2014). Focusing on the recent social impact on the demand for life insurance in the EU, we highlight two main research questions: (1) Is there a statistically significant relationship between life insurance and social determinants? (2) Is there a relationship between life insurance and social security in the form of social insurance (pension, health care)? The empirical results of the study are presented below.

2.3

Data and Method

To test the research questions, we use the Pearson correlation with linear dependence to assess the relationship between life insurance premiums and three groups of social determinants: demographic (aging-related), old dependency ratio (OD), life expectancy (LE) and population growth (PG); labour, average wages (AW), unemployment rate (UE) and self-employed (SE); and social protection, unemployment replacement rate (UR), health expenditures per capita (HE) and Gini index (GI). We use the OECD and the World Bank data for 2007–2017 to assess the social determinants on life insurance in 24 EU countries. The data on life insurance premiums were obtained from OECD statistics, other indicators from the World Bank. Some countries were excluded from the study due to the lack of data on some

2 Social Determinants of Life Insurance in the European Union

23

indicators or gaps over the years; the United States was included for comparison. We present the description of the variables, the average time series (Mean) and standard deviation (Stdev) in Table 2.5 (Appendix).

2.4

Results

A variable relationship is shown between life insurance and the selected social determinants (Table 2.2). The correlation results demonstrate a higher positive relationship with the Gini index, health-care expenditures per capita and a less positive relationship with average wages. It can be concluded that life insurance is closely related to the state of poverty in society: with the growth of this indicator, the precautionary demand for life insurance grows. A similar link is present in the relationship between spending on health care and life insurance as a way to compensate personal health expenditures. The resulting positive effect from the average wage is associated with a known effect: with an increase in income, there is an incentive to save, including life insurance. Other selected social determinants do not have a statistically significant effect on the demand for life insurance. Since the average results represent a generalized picture, we also evaluated the following interrelationships between the countries of the EU (Table 2.1). Most countries show a significant positive (negative) relationship between the selected social indicators and the demand for life insurance. It is noteworthy that the same indicators may have different effects on the direction of life insurance. The Netherlands, Greece and Italy have the highest average values of the impact of social indicators on the demand for life insurance, while Norway, France and Slovakia have the lowest. At the country level, we see a clearer relationship between the social environment and life insurance and the impact of state social protection on such insurance (Tables 2.3 and 2.4).

Table 2.2 Life insurance by type of contract (in million euros) in Europe, 2015–2016

Individual Group Non-unit-linked Unit-linked

2015 364,736 97,332 371,775 103,642

2016 352,403 93,007 339,314 119,541

Insurance Data. Data is provided by Insurance Europe members (the members of the European Union (except Lithuania), Switzerland, Iceland, Norway, Liechtenstein and Turkey), https://www. insuranceeurope.eu/insurancedata Source: Insurance Data (2019)

0.52 0.72 0.45* 0.85

0.67 0.73

0.71 0.52 0.77 0.59 0.81 0.63

0.75 0.67

0.61 0.54 0.83 0.56 0.63 0.9

0.9 0.77

0.85 0.54 0.58

0.68 0.61 0.92

0.6 0.68 0.83 0.79 0.53

LE

0.72 0.48* 0.79 0.73 0.41*

OD

0.87

0.53 0.85 0.38**

0.57

0.51* 0.55 0.54

0.49* 0.63 0.81 0.74 0.59 0.88

0.64

0.83

0.53

0.41* 0.79 0.46* 0.97

0.65 0.68 0.49* 0.89 0.86

0.79

0.64 0.67 0.59 0.68 0.38**

AW

0.38**

PG

0.37**

0.59

0.69 0.53 0.36** 0.52 0.82 0.77 0.4**

0.84 0.67 0.75

0.44* 0.65 0.55* 0.35** 0.78

0.61 0.48*

UE

P-value at significant level, significance at the level without * 0.01; with * 0.10; with **0.05 – 0.1

Country Austria Belgium Czech Republic Denmark Finland France Germany Greece Hungary Iceland Ireland Italy Luxembourg Netherlands Norway Poland Portugal Slovak Republic Slovenia Spain Sweden Switzerland United Kingdom United States

Table 2.3 Country’s correlations

0.69 0.67 0.48* 0.41* 0.65 0.79 0.54 0.69 0.91

0.72 0.45* 0.92

0.53

0.36**

0.58 0.55 0.61

0.45*

SE

0.71

0.41* 0.7

0.71

0.53 0.73 0.67 0.65 0.47* 0.77

0.69 0.95 0.57 0.37** 0.57

UR

0.58 0.54 0.73 0.73 0.62 0.65 0.7 0.7 0.88

0.73 0.78 0.49* 0.7 0.87 0.74 0.82

0.68 0.82 0.75 0.73 0.63 0.49*

HE

0.57 0.48* 0.59 0.81 0.48* 0.61 0.76 0.76

0.61

0.82 0.73 0.63 0.4** 0.54 0.86

0.7 0.35** 0.64 0.71

GI

24 N. Grishchenko

2 Social Determinants of Life Insurance in the European Union

25

Table 2.4 Ranked country’s average correlation (by module) Higher relationship Netherlands Greece Italy Sweden United States Czech Republic United Kingdom Slovenia

2.5

0.77 0.71 0.69 0.63 0.61 0.60 0.60 0.60

Medium relationship Iceland 0.57 Denmark 0.57 Belgium 0.56 Austria 0.56 Switzerland 0.56 Hungary 0.55 Spain 0.52 Portugal 0.51

Low relationship Ireland Poland Finland Germany Luxembourg Slovak Republic France Norway

0.51 0.50 0.50 0.44 0.41 0.40 0.29 0.19

Conclusions

In this study, we evaluate the relationship between life insurance and social determinants (demographic, labour and social protection) on the example of 24 countries of the EU for 2007–2017. We found that not all selected social indicators have positive or negative effects on the demand for life insurance. The most significant impacts are by the Gini index, health expenditures and average wages. The country analysis provides a more individual picture of countries most and less affected by the social factors associated with life insurance. We can conclude that life insurance is highly dependent on the situation of poverty in the country’s economy and the level of personal health-care expenditure. This is important to consider for the future effects of social policy. On the contrary, the most frequently indicators such as old dependency or life expectancy, which are associated with the aging of the population and should affect pension savings through life insurance, have not received support. However, with respect to health (medical) insurance, such connection is present, possibly due to a more sensitive dependency. The relationship between life insurance and social determinants in the EU shows a tendency of sufficiently strong differentiation, the reasons for which can serve as a basis for future research. The presence (absence) of a relationship between social determinants and life insurance is important to take into account when determining social policies and also for the development of the life insurance market. The limitations of the results obtained are associated with the certain set of social indicators. Acknowledgement We are grateful to the HSE Academic Writing Center for correcting an earlier version of this paper.

Appendix

31,214

233,137

783,739

Switzerland

United Kingdom

United States

20,338

257

Slovenia

Sweden

692

Slovak Republic

18,053

7373

Portugal

Spain

9649

Poland

20,409

Luxembourg

25,233

51,728

Italy

12,586

39,034

Ireland

Norway

36

Iceland

Netherlands

1147

106,360

Germany

1729

88,426

France

Hungary

5638

Greece

22,560

1413

Czech Republic

Finland

6427

Denmark

2676

Belgium

3447

50,171

83,925

3649

9266

11,282

313

772

3849

2748

1365

8166

4279

17,959

4893

9

793

1019

7872

65,725

1438

1976

1461

11,388

20.6

26.5

25.6

29.3

26.7

25.3

18.5

29.7

20.6

23.6

24.9

20.4

32.9

18.2

19.3

24.6

28.8

31.4

27.9

28.7

27.2

24.1

27.2

27.0

1.7

1.7

1.3

1.8

1.8

1.9

1.7

2.3

2.1

1.3

2.6

0.3

2.2

2.2

1.6

1.6

1.6

0.9

2.3

3.4

2.5

3.0

0.9

1.3

4

Mean

78.5

80.6

82.7

81.8

82.4

80.0

75.9

80.1

76.7

81.5

81.1

81.4

82.3

80.9

82.2

74.9

80.7

80.4

82.0

80.5

79.8

78.0

80.6

81.0

5

Stdev

0.3

0.7

0.6

0.5

0.9

1.0

1.0

1.2

0.9

0.8

0.6

1.1

0.7

0.7

0.5

1.0

0.7

0.5

0.5

0.8

1.0

0.9

0.6

0.5

6

7

0.8

0.7

1.1

0.9

0.4

0.3

0.1

0.2

0.0

1.1

0.4

2.1

0.4

1.1

1.1

0.3

0.2

0.0

0.5

0.4

0.5

0.3

0.7

0.6

Mean

0.1

0.1

0.1

0.2

0.7

0.3

0.0

0.3

0.1

0.2

0.1

0.3

0.4

0.8

0.9

0.1

0.4

0.7

0.1

0.1

0.1

0.2

0.3

0.3

8

Stdev

58,802

43,692

61,164

39,899

38,835

33,501

22,043

25,812

23,901

49,167

52,470

59,793

37,033

47,842

50,244

21,112

28,838

44,532

41,700

42,498

49,594

22,834

49,605

49,284

9

Mean

1313

678

1439

1670

1079

876

1276

678

1541

2411

947

1825

576

2174

5725

747

2889

1872

1359

574

1497

1118

658

706

10

Stdev

1

Stdev

Mean

Stdev

2

Mean

3

Average wages (AW)

Population growth (PG)

Life insurance demand (LID)

Austria

Country

Social indicators

Labour

Old age dependency (OD)

Life expectancy (LE)

Demographic (aging-related)

Table 2.5 Descriptive statistics by country

6.8

6.4

4.4

7.5

19.3

7.5

11.9

11.5

8.4

3.5

5.2

5.3

9.8

10.9

4.8

8.4

18.6

5.9

9.1

8.1

6.1

5.7

7.8

5.2

11

Mean

2.0

1.4

0.5

0.8

5.6

1.9

2.1

2.9

1.8

0.7

1.6

0.8

2.4

3.8

2.0

2.5

7.6

1.6

1.1

0.9

1.4

1.5

0.6

0.6

12

Stdev

Unemployment (UE)

6.7

14.4

15.4

10.4

17.1

16.1

15.1

21.3

22.1

7.3

15.3

8.7

24.8

16.5

12.4

11.6

35.5

11.2

11.4

13.5

8.9

17.3

14.6

13.2

13

Mean

0.3

0.8

0.4

0.3

0.5

1.4

0.9

2.7

1.0

0.5

1.5

1.1

0.8

0.5

0.5

0.8

0.9

0.6

0.4

0.5

0.3

0.8

0.3

0.4

14

Stdev

Self-employed (SE)

Social protection

61.6

53.3

73.2

68.2

77.9

81.6

61.9

93.8

69.6

67.4

74.4

83.5

73.7

60.3

78.9

69.7

42.8

59.5

68.7

67.8

84.8

74.0

85.4

66.9

15

Mean

0.8

2.8

1.0

5.4

0.8

5.0

0.9

26.3

3.1

0.5

2.0

1.6

3.9

2.7

2.2

2.5

7.0

0.9

0.5

3.9

0.9

6.6

4.3

5.4

16

Stdev

Unemployment replacement rate (UR)

8394.3

3378.6

6371.6

4448.2

2940.9

2486.4

1921.4

2567.9

1432.2

5566.4

4852.7

6070.8

3176.6

4690.8

3692.1

1680.1

2449.7

4610.7

4236.9

3644.1

4523.2

2110.9

4144.8

4601.0

17

Mean

888.9

580.3

974.1

864.8

190.0

203.7

201.7

124.7

245.4

615.4

446.3

404.6

174.7

509.8

272.4

188.1

301.8

626.9

435.0

393.2

463.4

3155

481.0

529.4

18

Stdev

Health expenditures per capita (HE)

40.8

33.8

32.7

28.0

35.3

25.1

26.5

36.0

32.8

26.4

28.3

32.1

34.5

32.3

27.9

29.4

34.9

30.9

32.9

27.5

27.3

26.2

28.2

30.7

19

Mean

0.4

1,0

0.9

0.7

0.8

0.8

1.0

0.6

0.7

0.7

0.7

1.3

0.8

0.8

1.9

1.6

1.1

0.5

0.5

0.5

1.1

0.3

0.5

0.4

20

Stdev

Gini index (GI)

26 N. Grishchenko

2 Social Determinants of Life Insurance in the European Union

27

References Alhassan, A. L., & Biekpe, N. (2016). Determinants of life insurance consumption in Africa. Research in International Business and Finance, 37, 17–27. Arltova, M., & Kabrt, T. (2018). Analysis of determinants, influencing life insurance demand in the Czech Republic. Politicka Ekonomie, 66(3), 344–365. Borda, M., Kowalczyk-Rólczyńska, P., & Grishchenko, N. (2017). Social health insurance reforms – Mid-term experiences of Poland and Russia. Journal of US-China Public Administration, 14(5), 254–262. Burić, M. N., Smolović, J. C., Božović, M. L., & Filipović, A. L. (2017). Impact of economic factors on life insurance development in Western Balkan countries. Zbornik Radova Ekonomskog Fakulteta u Rijeci, 35(2), 331–352. Cavapozzi, D., Trevisan, E., & Weber, G. (2013). Life insurance investment and stock market participation in Europe. Advances in Life Course Research, 18, 96–106. Celik, S., & Kayali, M. M. (2009). Determinants of demand for life insurance in European countries. Problems and Perspectives in Management, 7(3), 32–37. Feyen, E., Lester, R., & Rocha, R. (2011). What drives the development of the insurance sector? An empirical analysis based on a panel of developed and developing countries (Policy Research Working Paper 5572). World Bank. Firtescu, B. (2014). Influence factors on European life insurance market during crises. Procedia Economics and Finance, 16, 348–355. Grishchenko, N. (2019). Rationality of life insurance behavior under economic uncertainty. Journal of International Business and Economics, 7(1), 18–25. Han, N.-W., & Hung, M.-W. (2017). Optimal consumption, portfolio, and life insurance policies under interest rate and inflation risks. Insurance: Mathematics & Economics, 7, 54–67. Hou, H., Cheng, S.-Y., & Yu, C.-P. (2012). Life insurance and Euro zone’s economic growth. Procedia – Social and Behavioral Sciences, 57, 126–131. Insurance Data. (2019). Retrieved from https://www.insuranceeurope.eu/insurancedata Kjosevski, J. (2012). The determinants of life insurance demand in Central and South-Eastern Europe. International Journal of Economics and Finance, 4(3), 237–247. Lim, B. H., & Kwak, M. (2016). Bequest motive and incentive to retire: Consumption, investment, retirement, and life insurance strategies. Finance Research Letters, 16, 19–27. Lin, C., Hsiao, Y.-J., & Yehc, C.-Y. (2017). Financial literacy, financial advisors, and information sources on demand for life insurance. Pacific-Basin Finance Journal, 43, 218–237. Mahdzan, N. S., & Victorian, S. M. P. (2013). The determinants of life insurance demand: A focus on saving motives and financial literacy. Asian Social Science, 9(5), 274–284. Millo, G., & Carmeci, G. (2014). A subregional panel data analysis of life insurance consumption in Italy. The Journal of Risk and Insurance, 82(2), 317–340. Outreville, J. F. (2018). Culture and life insurance ownership: Is it an issue? Journal of Insurance Issues, 41(2), 168–192. Shi, X., Wang, H.-J., & Xing, C. (2015). The role of life insurance in an emerging economy: Human capital protection assets allocation and social interaction. Journal of Banking & Finance, 50, 19–33. Sliwinski, A., Michalski, T., & Roszkiewicz, M. (2013). Demand for life insurance – An empirical analysis in the case of Poland. The Geneva Papers, 38, 62–87. Statista. (2019). Retrieved from https://www.statista.com/ Wang, K.-M., Lee, Y.-M., Lin, C.-L., & Tsai, Y.-C. (2018). The effects of health shocks on life insurance consumption, economic growth, and health expenditure: A dynamic time and space analysis. Sustainable Cities and Society, 37, 34–56.

Chapter 3

The Challenges Faced by Life Insurance Companies in the Baltic States Ramona Rupeika-Apoga, Inna Romānova, and Simon Grima

3.1

Introduction

Life insurers are significant participants in Europe’s economy, not least as investors. They are not immune to the after-effects of the crisis. Most noticeably, low interest rates and high capital requirements are impacting on profitability and starting to drive strategic decisions. Herein, we aim to lay out trends on life insurers in the small European Union (EU) Baltic States, specifically Latvia, Estonia and Lithuania, all of which considered small because of the size of their population which is below three million, respectively. Similar to other insurance firms within small EU States, firms within these countries are struggling to balance the pressures of regulation and competition in a way that will yield a successful strategy. There are several differences between the individual EU countries in relation to life insurance. Notwithstanding a single market, there are strong national drivers, which include tax arrangements and pension provision, which affect product design, and the business strategy. Also, while many EU markets have shifted towards unitlinked business, several products which were written when yields were higher still prevail. Moreover, since life insurance liabilities are of a longer duration than the underlying backing assets, insurers are required to fund these liabilities in markets which differ from the ones in which they were issued. Since expectations of life insurance market participants is that life insurers will continue to offer products based on guaranteed returns, insurers need to fund products based on current trends and not without relying on historic prices and

R. Rupeika-Apoga · I. Romānova University of Latvia, Riga, Latvia S. Grima (*) University of Malta, Msida, Malta e-mail: [email protected] © Springer Nature Switzerland AG 2020 M. Borda et al. (eds.), Life Insurance in Europe, Financial and Monetary Policy Studies 50, https://doi.org/10.1007/978-3-030-49655-5_3

29

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R. Rupeika-Apoga et al.

rates of return. This since in these markets, insurers cannot pass the investment and interest rate risks wholly to policyholders. This study adds value to the findings of various prominent researchers such as King (1993), Briguglio (1995), Baldacchino (2006), Bezzina and Grima (2012) and Bezzina et al. (2014), who highlight the importance of the use of small States as small-scale laboratories for more complex politics, regulations and policies of larger countries. Moreover, this study helps voice the cries of smaller States, which are many a times dampened by the members of larger States, and allows for an understanding of the impact and implications of challenges to smaller jurisdictions, in this case within the EU. This hopefully will help to strengthen the effectiveness of competitiveness, regulations and common policy within the EU. We base our analysis on the examination of aggregate statistical data of the Estonian, Latvian and Lithuanian insurance sector provided by the Finantsinspektsioon (financial market regulator in Estonia), the Financial and Capital Market Commission (financial market regulator in Latvia) and the Central Bank of Lithuania (responsible for the supervision of financial market in Lithuania), Eurostat data as well as annual reports of the insurance companies in the three Baltic States.

3.2

The Insurance Market Within the Baltic States

The insurance market in the Baltic States is relatively young; it has started to develop in the early 1920s till the Second World War. In the period between 1940 and 1991, the Baltic States were a part of the Union of Soviet Socialist Republics (USSR) and consequently a part of the centralised financial and insurance system of the USSR. Consequently, in the 1990s, when the Baltic countries have regained their independence, the financial system and insurance system, in particular, had to be re-established. Regaining of independence and transition to market economy asked for systemic changes in the economies of the Baltic States. A whole package of reforms was introduced, concerning macroeconomic stabilisation, privatisation, liberalisation of prices and trade, etc. (Åslund and Dombrovskis 2011). Vitally important was also monetary stabilisation and the introduction of the national currencies1 as well as the establishment of the financial systems to ensure a well-functioning market economy. Over the next few years, the financial systems were built to support economic development of the three Baltic countries, although, already in the middle of the 1990s, the financial sector has experienced a number of problems due to high inflation, drop of the production volume and a decrease of the real GDP and of investment opportunities. Difficulties were also related to

1 The Estonian kroon (EEK) was introduced in 1992. The Latvian lat (LVL) was introduced in 1993. The Lithuanian litas (LTL) was introduced in 1993.

3 The Challenges Faced by Life Insurance Companies in the Baltic States

31

undigested legislation, e.g. in the field of real estate, absence of collateral registry, no deposit guarantees, etc.

3.3

Formation of the Insurance Market in the Baltic States

During the first 20 years of independence, national legislation was developed, and several national private insurance companies began operations within the Baltic States. During the Soviet era, the Baltic insurance market was subject to a state monopoly with the only insurer being Gosstrah. It operated until 1990. Following the restoration of independence, most things had to be restarted, which in turn laid the foundations for the creation of an insurance market in the Baltic States. In the early 1990s, the Baltic States developed their own national financial markets, with a strong focus on the development of the financial sector and privatisation of financial intermediaries. The legislation framework for the insurance market was created (in Lithuania in 1990, Estonia in 1992 and Latvia in 1993); and regulating and supervising authorities were established. The privatisation process of the state insurance companies, which assumed the obligations of Gosstrah, was completed in 1995–1997 (Bokans 2018). The Baltic countries received significant assistance from the EU countries, especially Germany, in the creation of new financial markets, including insurance. New insurance laws were enacted in Lithuania (1996) and in Latvia (1998). Moreover, life insurance was separated from non-life insurance in Estonia in 1992; in Latvia, in 1994; and in Lithuania, in 1996. In connection with the transition of the economy from a planned to a market economy, insurance markets in the Baltic countries had to be created in a very short time, that is, in just 10 years. In 2004, all three countries joined the EU, and in 2011 Estonia joined the euro area, and in 2014 and 2015 Latvia and Lithuania joined it, respectively. Now insurance accounting complies with EU requirements.

3.3.1

Market Analysis

In the Baltic States, many companies having their registered office in one of the three countries have branch representations in the other two member states. The insurance market in the Baltic States is divided into two parts, non-life insurance and life insurance, with the non-life insurance dominating the Baltic market. This can be explained by the obligatory motor vehicle third-party liability compulsory insurance (with 21.11% of the gross premiums in 2018) as well as relatively low welfare and income in the countries. The share of life insurance as of the total insurance business in the Baltic States is very low when compared to the average European figures (see Fig. 3.1). In Latvia the share of life insurance is only 15%, in Estonia 17% and in Lithuania 25%, with an average share in Europe of 58% during 2018 (IE 2019).

32

R. Rupeika-Apoga et al.

Fig. 3.1 Insurance gross premiums in 2018 in the Baltic States, mln. EUR and %. Source: adapted from Statistics Estonia, Bank of Lithuania and FCMC Latvia

Fig. 3.2 Life insurance gross premiums in the Baltic States, 2014–2018, mln. EUR. Source: adapted from Statistics Estonia, Bank of Lithuania and FCMC Latvia

We can observe a steady growth in life insurance gross premiums in Estonia and Latvia, while the Lithuanian life insurance sector is showing larger fluctuations during the period from 2014 to 2018 (see Fig. 3.2). Much larger volume of the premiums paid in Lithuania in comparison to the neighbour countries can be

3 The Challenges Faced by Life Insurance Companies in the Baltic States

300

33

1320 1300

250

1260

150

1240

Europe

Baltic States

1280 200

1220

100

1200 50

1180

0

1160 2014

2015 Estonia

2016 Latvia

2017 Lithuania

2018 Europe

Fig. 3.3 Life insurance density (total premiums per inhabitant) by country, 2014–2018, EUR. Source: adapted from Statistics Estonia, Bank of Lithuania, FCMC Latvia and Insurance Europe

explained by a larger market in Lithuania (2.8 million inhabitants in Lithuania, 1.3 million inhabitants in Estonia, 1.9 million in Latvia). When looking at international scenarios, the life insurance density indicating total premiums per inhabitant shows that life premiums per capita during the last 5 years were slowly growing in all three Baltic States, but they are still very far behind the European average indicator (see Fig. 3.3). During this period, the average life insurance premium in the Baltic States was 63.4 EUR (65.9 EUR in Estonia, 50.8 EUR in Latvia, 73.6 EUR in Lithuania), that is, 20 times smaller in comparison to the European average (1275 EUR per inhabitant). The penetration rate (the ratio of the premium underwritten to the GDP of the country) shows that the level of the insurance sector development in the life insurance market in all three Baltic States is far behind the average penetration rate in Europe (see Fig. 3.4). In 2018 life insurance penetration rates in all three countries (0.37% in Estonia; 0.40% in Latvia and 0.48% in Lithuania) are approximately ten times lower compared to the average penetration rate of the life insurance in Europe (4.49%). Thus, despite the steady growth of the life insurance market, it still has a huge potential to grow in the future. The Baltic life insurance market is relatively small due to the demographics and the relatively low income level. Therefore, the majority of the companies operating in the market choose to provide products in all Baltic States. There are five life insurance companies operating in all three countries: Mandatum, Ergo, Compensa and the market leaders Swedbank Life Insurance and SEB Life Insurance holding more than 60% of the market (Fig. 3.5).

34

R. Rupeika-Apoga et al.

Fig. 3.4 Life insurance penetration in the Baltic States and Europe in 2018, %. Source: adapted from Statistics Estonia, Bank of Lithuania, FCMC Latvia and Insurance Europe

Fig. 3.5 Gross premiums written of five companies operating in the all Baltic States in 2018, mln. EUR. Source: adapted from Statistics Estonia, Bank of Lithuania and FCMC Latvia

As noted above, since the Baltic market is relatively small, with a total population of 6.04 million inhabitants, it was decided by most life insurance companies to consolidate their business, and one legal entity was built for the operations in all three countries (e.g. Ergo life insurance in 2012 and non-life insurance in 2013; SEB Life Insurance in 2018). The consolidation is seen as a key success factors to reach

3 The Challenges Faced by Life Insurance Companies in the Baltic States

35

higher operational efficiency and ensure compliance with the increased amount of regulations, which would add to the costs. Besides, the consolidation is seen as a way to provide better solutions for the clients. Life insurance is no longer just a financial support to the family after the death of the breadwinner. Nowadays life insurance has evolved to an investment product ensuring wealth building and tax benefits. Wealth building is especially important for small growing economies with relatively low income. In terms of GDP per capita, all the three Baltic countries reach approximately half of the EU’s average (average in 28 countries of the EU): GDP per capita in 2018 in Estonia was 15,090 EUR (53.4% of the EU average); Latvia, 12,180 EUR (43.1% of the EU average); and Lithuania, 13,310 EUR (47.1% of the EU average), making the wealth building effect of life insurance especially important. On the other hand, this limited livelihood of residents slows the development of the life insurance market in the Baltic States. Additional stimulus to the development of the life insurance market is given by the commercial banks that are highly recommending the borrowers to have a life insurance contract. The life insurance for the borrower is beneficiary for both parties: for the borrower as in the case of his/her death, the outstanding loan obligations do not rest on the survivors as well as for the bank due to a lower risk of credit default. The latest developments in the market reflect the fact that the main factors promoting development of the life insurance market in the Baltic States are economic growth and expanded activities of insurance companies outside the resident country, mainly in other Baltic States. On the other hand, requirements such as Solvency II on the EU life insurance sector which came into force in 2016 have increased difficulties of these firms mainly with regard to their management systems and technical documentation, the largest risk being that of identifying, measuring and monitoring risk in life insurance and of the technical provisions being underestimated because of the assumptions taken being too optimistic. This in turn could affect the management of risk and how the companies meet their liabilities. The main factors determining the negative performance of the life insurance companies (in 2018 EUR 3.7 million losses in Latvia) were the growth of compensation disbursements, increase of operating expenses and the negative fluctuations of the financial market—generating returns on investments of life insurance companies. Besides, the low level of interest rates makes it even more difficult for insurers to find investment opportunities with an adequate level of return and risk. The financial market development and negative fluctuations had a particularly larger impact on life insurance companies in the Baltic States due to their less conservative investment policy in financial instruments (such as stocks and investment fund shares) when compared to the non-life insurers who reported positive return on investment in 2018. In general, the Baltic life insurance companies are stable, ensuring an adequate level of capital, which is reflected in high figures of solvency ratios. The solvency ratio, measuring the proportion of the insurers’ capital to the solvency or minimum capital requirement, is above the lower threshold of 100% in all three Baltic States (consolidated solvency indicator of 155% in Estonia, 132% in Latvia, 243% in

36

R. Rupeika-Apoga et al.

Lithuania).2 This is an important indicator showing the stability of the companies and their ability to ensure better protection of the interests of clients (policy holders).

3.4

Pensions and Tax Incentives

All three Baltic countries have introduced a modern three-pillar pension system. The first two pension pillars depend on the social contributions of current taxpayers (approximately 30–33% in the Baltic countries), and the third pension pillar is based on voluntary contributions. The first pillar is mainly oriented towards the principles of solidarity and social protection, providing minimum pensions for all residents. The second pension pillar allows to accumulate a part of social contributions on individual accounts, which can be used to generate additional profit and is a subject for life insurance providers. The idea underlying the second pillar is to shift the responsibility of the state to individuals, private pension funds and insurance companies for providing additional income to pensioners. As a result, in Lithuania and Estonia, state-funded contributions to the second pillar are combined with individual co-payments (2%), while in Latvia, they are only state funded. Additionally, in Lithuania and Estonia, it is possible to inherit these assets. From 2013 in Lithuania, participation in co-payment schemes gives an additional state-funded contribution of 1–2% of the average wage in the country. The accumulated second pillar capital can be obtained upon reaching retirement age, when an individual can choose life pension insurance or add the accumulated capital of the second pillar to the pension of the first pillar. For example, in Latvia from 2013 to 2017, the State Social Insurance Agency has received information from insurance companies on more than 5630 annuity contracts that have come into effect (SSIA 2019). The popularity of annuity products is growing in all three Baltic States; as a lifetime pension is paid for life, the contract may specify the beneficiary who will receive the pension if the insured dies. Thus, the insurance products that are combined with a pension scheme have a large potential to develop in the nearest future. Another potential driver of the further development of the life insurance in the Baltic States is the tax incentives. Tax incentives apply to savings in private pension funds and in insurance agreements. The third pillar scheme is voluntary but well supported by income tax refunds. In Latvia and Lithuania, tax incentives can also be used by employers who are saving for their employees. There are limits for the income tax refunds. In Estonia the tax refund can be redeemed by individuals only for their individual payments of 15% of gross salary and in Latvia and Lithuania— 20% and 25%, respectively, with shared limits for individuals and employers. There are also limitations on the age as individuals cannot access these assets from private

2

Information of the respective financial regulator

3 The Challenges Faced by Life Insurance Companies in the Baltic States

37

pension funds before they turn 55 and life insurance savings cannot be shorter than 5 to 10 years3 to enable the use of the tax benefits.

3.5

Lithuania

In Lithuania there are 20 companies providing insurance products, 8 of them offer life insurance products: ERGO Life Insurance SE, “Swedbank Life Insurance SE” Lietuvos filialas, Mandatum Life, “Compensa Life Vienna Insurance Group SE” Lietuvos filialas, SEB gyvybės draudimas, PZU Lietuva gyvybės draudimas, Aviva Lietuva and Bonum Publicum. The first five companies operate also in the other Baltic States. The products being offered in the life insurance markets are: • • • •

Insurance with profit participation Index-linked and unit-linked insurance Other life insurance Health insurance

It is important to note that the health insurance in Lithuania is considered to be a part of the life insurance. Therefore, to enable comparison with other countries in the Baltics where health insurance is not considered to be a part of the life insurance, we exclude it from the data on the life insurance. This was necessary due to the completely different type of coverage health insurance and life insurance offer. On the other hand, in non-life insurance markets, the insurers provide mainly the following products (see Fig. 3.6): • • • • • •

Motor vehicle liability insurance Other motor insurance Fire and other damage to property insurance Medical expense insurance Income protection insurance General liability insurance

Figure 3.6 shows the absolute dominance of the motor vehicle insurance (47.7%), followed by the index-linked and unit-linked insurance representing 17.8% of the gross premiums written by the insurers in Lithuania in 2018. Figure 3.7 shows the index-linked and unit-linked insurance is the most popular type of the life insurance representing 69.85% of the total premium written in Lithuania. In 2016 the Lithuanian market authority has changed the reporting standards; therefore, data for 2014 and 2015 are not comparable.

3 In 2018 the minimum term requirement for the life insurance contracts to use tax benefits was extended from 5 to 10 years in Latvia, which negatively affected the growth of the premiums paid.

38

R. Rupeika-Apoga et al.

Fig. 3.6 Lithuania: Life and main non-life insurance premiums by type of contract in 2018, mln. EUR. Source: adapted from Bank of Lithuania

Fig. 3.7 Life insurance types by gross premiums written from 2014 to 2018 in Lithuania, mln. EUR. Source: adapted from Bank of Lithuania

The life insurance gross premiums written and gross claims paid in Lithuania in the last 5 years (see Fig. 3.8) show that insurance companies benefits paid are significantly lower compared with average European figures as gross premiums

3 The Challenges Faced by Life Insurance Companies in the Baltic States

39

245

195

145

95 45

2014

2015 Gross premiums

2016

2017

2018

Gross claims paid

Fig. 3.8 Life insurance gross premiums and gross claims paid from 2014 to 2018 in Lithuania, mln. EUR. Source: adapted from Bank of Lithuania

written exceed premiums written by 89%, when in Europe it’s only 10%. It can be explained with the fact that the Baltic life insurance market is relatively young and there are not so many life insurance claims have as yet been made.

3.6

Estonia

In Estonia five companies (three Estonian life insurance companies and two branches of foreign insurers authorised to operate in Estonia) provide life insurance products: “Ergo Life Insurance SE Eesti filiaal”, “Swedbank Life Insurance SE”, “Mandatum Life Insurance Company Limited Eesti filiaal”, Compensa Life Vienna Insurance Group SE and “SEB Elu- ja Pensionikindlustuse AS”. All these companies are present in Lithuania and Latvia too. The share of life insurance is relatively small (17% in 2018) in comparison to the non-life insurance in Estonia (see Fig. 3.9) with the vehicle insurance, the property insurance and the motor third-party liability compulsory insurance with 72.6% of the total gross premiums paid dominating the insurance market in Estonia. The most popular insurance products in Estonian market are the unit-linked life insurance and insurance with profit-sharing. The breakdown of life insurance product types by gross premiums written is shown in Fig. 3.10, where term and whole life assurance makes up to 29%, pension insurance makes up to 26%, life insurance unitlinked contracts make up to 24% and supplementary insurance and endowment insurance make up to 11% and 10% of the gross premiums written, respectively, in 2018. The life insurance gross premiums written and gross claims paid in Estonia from 2014 to 2018 show that again benefits paid by the insurance companies are significantly lower compared with average European figures as gross premiums written

40

R. Rupeika-Apoga et al.

Unit linked life insurance Pension insurance Term and whole life assurance Endowment insurance Supplementary insurance Vehicles insurance Property insurance Motor TPL insurance General liability insurance Travel insurance Insurance for pecuniary loss Accident and sickness insurance 0

40

80

120

160

Fig. 3.9 Life and main non-life insurance premiums by type of contract in Estonia, 2018, mln. EUR. Source: adapted from Statistics Estonia

Fig. 3.10 Life insurance types by gross premiums written from 2014 to 2018 in Estonia, mln. EUR. Source: adapted from Statistics Estonia

exceed premiums written by 68%, when in Europe it’s only 10%. The explanation of this fact is similar to the Lithuanian case (Fig. 3.11).

3 The Challenges Faced by Life Insurance Companies in the Baltic States

41

Fig. 3.11 Life insurance gross premiums written and gross claims paid from 2014 to 2018 in Estonia, mln. EUR. Source: adapted from Statistics Estonia

Life insurance unit-linked contracts Life insurance with savings Annuity Life insurance without savings Transport ownership liability insurance Vehicle insurance OCTA Health insurance Property insurance Accident insurance 0

40

80

120

160

Fig. 3.12 Life and main non-life insurance premiums by type of contract in Latvia, 2018, mln. EUR. Source: adapted from FCMC Latvia

3.7

Latvia

In Latvia, life insurance products are offered by two local companies and four branches of foreign insurance companies, two from Estonia, one from Lithuania and one from Finland. In addition to life insurance products, life insurance companies can also provide health and accident insurance. However, Latvian life insurance companies only provide accident insurance with a market share of 12% of the gross premiums written in 2018 (Fig. 3.12). The breakdown of life insurance product types by gross premiums written is shown in Fig. 3.13, where life insurance unit-linked contracts play the dominant role

42

R. Rupeika-Apoga et al.

140 120 100 80 60 40 20 0 2014

2015

2016

2017

Life insurance unit-linked contracts

Life insurance with savings

Life insurance without savings

Annuity

2018

Fig. 3.13 Life insurance types by gross premiums written from 2014 to 2018 in Latvia, mln. EUR. Source: adapted from FCMC Latvia

130 120 110 100 90 80 70 60 50 40 30 2014

2015 Gross premiums

2016

2017

2018

Gross claims paid

Fig. 3.14 Life insurance gross premiums written and gross claims paid from 2014 to 2018 in Latvia, mln. EUR. Source: adapted from FCMC Latvia

with 48%, while the life insurance with savings makes up to 25%, annuity makes up to 22% and life insurance without savings is 5% in 2018. The life insurance gross premiums written and gross claims paid in Latvia from 2014 to 2018 show that again the claims paid by the life insurance companies are much lower compared with the average European figures as gross premiums exceed

3 The Challenges Faced by Life Insurance Companies in the Baltic States

43

claims paid by 35% (Fig. 3.14); however, the difference is the smallest within three Baltic States.

3.8

Conclusions

The Baltic life insurance market is a young growing market. But it is still underdeveloped in comparison with the western European countries. The life insurance density in the Baltic States is 20 times smaller compared to the European average (1275 EUR per inhabitant). Besides, the share of the life insurance in the Baltic markets is much lower (15% in Latvia, 17% in Estonia and 25% in Lithuania 25%) than the average share in Europe (58%). Low interest rates and negative fluctuations in the financial market deteriorated the investment opportunities of the life insurers. As a result of the more risky investment policy in financial instruments, the life insurance companies in the three Baltic States reported negative returns on investments in 2018. An important function of the life insurance is to provide the clients with the personal financial stability, enabling higher pension in the future, as well as enabling diversification of investments and building of welfare. The latest is extremely important for small growing economies with relatively low level of prosperity: in terms of GDP per capita, all three Baltic countries reach only approximately half of the EU average. Besides, the limited welfare of inhabitants retards the development of the life insurance market in the Baltic States. Taking into account the relatively small size of the life insurance market in the Baltic States, the life insurance companies mostly follow the consolidation path to reach higher operational efficiency as increased amount of regulations require additional investments. Consolidation may also be beneficiary to the clients, enabling better solutions due to the economy of scale. This may be a solution for other small EU States such as Malta, Slovenia, Cyprus and Luxembourg which all have populations below three million and although have different characteristics emerging from history, country proximity and culture can benefit from such consolidation. Moreover, larger States, regulators and policy makers can also benefit from the experience of these small States and use their experiences to devise policies and regulations that can benefit all EU States without putting unnecessary pressures on the smaller countries which can be seen as barriers to offering good life insurance policies at the detriment of EU citizens. Acknowledgement The chapter was supported by the project “INTERFRAME-LV”.

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References Åslund, A., & Dombrovskis, V. (2011). How Latvia came through the financial crisis. Washington, DC: Peterson Institute for International Economics. Baldacchino, G. (2006). Islands, island studies. Island Studies Journal, 1(1), 3–18. Bank of Lithuania. (2019). Data. Available at https://www.lb.lt/en/insurers-performance-indicators Bezzina, F., & Grima, S. (2012). Exploring factors affecting the proper use of derivatives: An empirical study with active users and controllers of derivatives. Manag Financ, 38(4), 414–434. Bezzina, F., Grima, S., & Mamo, J. (2014). Risk management practices adopted by financial firms in Malta, managerial finance. Managerial Finance, 40(6), 587–612. Bokans, J. (2018). Report life insurance market development in Baltic countries. OECD. Available at http://www.oecd.org/finance/insurance/1868447.pdf Financial and Capital Market Commission of Latvia Data. (2019). Available at https://www.fktk.lv/ en/statistics/insurance/quarterly-reports/ Insurance Europe. (2019). European insurance – Key factors. Available at https://www. insuranceeurope.eu/sites/default/files/attachments/European%20insurance%20%E2%80%94% 20Key%20facts.pdf King, R. (1993). The geographical fascination of islands. In D. G. Lockhart, D. Drakakis-Smith, & J. Schembri (Eds.), The development process in Small Island states (pp. 13–37). London: Routledge. State Social Insurance Agency. (2019). Available at https://www.vsaa.gov.lv/en/services/forseniors/ Statistics Estonia. (2019). Available at https://www.stat.ee/53733?highlight¼insurance

Chapter 4

The Turkish Life Insurance Market: An Evaluation of the Current Situation and Future Challenges Ercan Ozen and Simon Grima

4.1

Introduction

Turkey’s population peeked 82 million at the end of 2018. According to the World Bank data (2019), gross domestic product (GDP) was USD 766.509 million as at the end of 2018. The current account balance as a % of GDP in the same period was
3.555% and the savings as a % of GDP was 26.6%. In 2018, the country ranked 18th in GDP terms amoung the largest economies in the world. The annual export figures stood at USD 168.023 million and imports amounted to USD 223.046 million. Borsa Istanbul market capitalisation stood at USD 149.263 million. Besides, according to the Turkey Statistical Institute data as of August 2019, the unemployment rate was around 14.0% (World Bank 2019). Economic growth in developing countries such as Turkey largely depends on the steady and healthy increase in domestic savings. That is the steady growth of domestic savings in banks, stock exchanges, insurance companies and other financial institutions with their different characteristics. Therefore, an increase in life insurance premiums can help trigger economic growth with the potential to generate an increase of funds in the economy and help individuals and businesses cover or transfer risks they do not want to carry (Outreville 2013). However, as noted from Fig. 4.1, Turkey’s economy is still not stabilised; its GDP per capita in 2009 was USD 9.038, while income per capita reached USD 12.519 in 2013. However, permanent growth could not be sustained and per capita income decreased to USD 9.311 in 2018 (World Bank 2019; Turkish Statistical Institute 2019).

E. Ozen University of Uşak, Uşak, Turkey S. Grima (*) University of Malta, Msida, Malta e-mail: [email protected] © Springer Nature Switzerland AG 2020 M. Borda et al. (eds.), Life Insurance in Europe, Financial and Monetary Policy Studies 50, https://doi.org/10.1007/978-3-030-49655-5_4

45

46

E. Ozen and S. Grima 14000

USD

12000

10000

8000

6000

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Years

Fig. 4.1 Gross domestic product per capita of Turkey. Source: Adapted from World Bank (2019)

Therefore, a study of the factors and trends that impact the take-up of life insurance in Turkey is needed to help in the stabilisation and sustainability of economic growth.

4.2

The Life Insurance Market in the EU and OECD Countries

World economic growth was 3.3% in 2017 and 3.2% in 2018. GDP growth in 2019 is expected to be 2.8% in the world and 1.1% in the Eurozone. Global premium production in 2018 was USD 5.193 billion. This value represents 6% of the world GDP. While the world insurance sector has decelerated in western countries, it is predicted that the world insurance sector will continue to grow, thanks to the expected increase in life insurance premiums, especially in developing Asian-Pacific countries (Swiss Re 2019). According to Insurance Europe (2019) data, the total amount of insurance premiums, generated in 2018, was Euro 1.311 billion. The amount of claims for these premiums reached Euro1.069 billion. 58% of the premiums generated during 2018 (Euro 764 billion) consist of life insurance premiums. Life insurance payments to the insured were equal to 65.95% of total payments (IE 2019). While the world insurance penetration rate is around 6%, it is 8.9% in OECD countries (OECD N.D.). Table 4.1 shows the ratio of insurance premiums to GDP in OECD countries. It highlights that the penetration rate is higher than the OECD average in northern European countries such as Denmark, Ireland, the Netherlands and the United Kingdom and in the United States and some central European countries. Luxembourg has the highest penetration rate among OECD countries with 38.8%.

Country Australia Austria Belgium Canada Chile Czech Republic Denmark Estonia Finland France Germany Greece Hungary Iceland Ireland Israel Italy Japan Korea Latvia Lithuania Luxembourg Mexico Netherlands

7.6

– 30.1

3.6

6.5

3.4 8.9

9.0

Year 2007 6.1

2008 5.6 5.1 8.1 7.7 – 3.5 9.2 – 3.2 11.1 6.4 2.2 3.2 2.7 23.0 4.9 6.4 7.3 10.2 – – 28.4 1.7 7.4

2009 5.2 5.5 8.0 6.6 – 3.6 – – 3.1 9.1 – 2.2 3.1 2.6 18.9 5.0 5.8 8.0 10.1 – – 44.9 1.7 7.2

Table 4.1 Total insurance penetration in OECD countries 2010 5.0 5.7 7.9 5.0 3.6 3.9 – 1.7 3.7 10.3 – – 3.1 2.1 19.5 4.9 7.5 8.5 10.2 1.9 1.6 51.2 1.9 5.8

2011 4.9 5.6 7.5 4.9 3.8 3.8 9.8 2.9 4.6 10.2 – 2.3 2.9 2.8 20.1 4.8 7.8 8.4 10.4 1.8 1.6 31.3 1.8 6.4

2012 4.8 5.3 8.2 4.8 3.8 3.7 9.9 2.4 3.6 9.2 6.6 2.4 2.6 2.7 18.4 4.8 6.7 8.8 10.8 1.9 1.6 43.8 1.9 6.0

2013 5.1 5.1 7.0 4.9 4.2 3.8 9.9 2.7 3.6 8.6 6.7 2.3 2.6 2.7 19.8 4.8 6.5 6.8 12.7 2.0 1.6 39.6 1.9 5.7

2014 6.0 5.1 7.0 4.8 4.2 3.6 10.4 3.1 4.8 8.8 6.5 2.2 2.5 2.6 18.6 4.8 7.4 6.4 12.3 2.1 1.6 43.2 2.1 5.4

2015 5.6 5.1 6.5 4.3 4.2 3.2 10.5 3.1 5.1 9.2 6.3 2.1 2.4 2.4 19.9 4.6 8.8 6.8 12.6 2.2 1.7 36.9 2.0 4.8

2016 4.9 5.0 6.3 4.4 4.6 3.0 10.4 3.3 5.1 9.4 6.1 2.0 2.5 2.4 16.4 4.7 8.9 6.8 12.6 2.2 1.8 34.3 2.1 9.8

(continued)

2017 4.4 4.7 6.1 – 4.9 2.9 11.0 3.4 4.1 10.7 6.3 2.0 2.5 2.3 17.5 4.7 7.9 7.7 12.4 2.1 1.9 38.8 2.2 9.5

4 The Turkish Life Insurance Market: An Evaluation of the Current Situation and. . . 47

19.8 10.8 9.2

8.7

7.6 2.7 5.4

Year 2007 2.4

2008 2.3 4.9 3.6 8.4 3.0 5.3 5.0 6.0 9.6 1.2 14.9 10.9 8.7

Source: Adapted from OECD Statistics (N.D)

Country New Zealand Norway Poland Portugal Slovak Republic Slovenia Spain Sweden Switzerland Turkey United Kingdom United States OECD – Total

Table 4.1 (continued) 2009 2.4 5.2 4.6 7.9 3.2 5.3 5.3 5.7 9.1 – 13.5 11.1 9.2

2010 2.3 5.5 3.7 8.6 3.1 5.3 5.6 5.4 9.0 – 12.5 10.8 8.7

2011 – 4.8 3.7 6.1 3.0 5.2 5.2 4.9 9.0 1.2 11.9 11.0 8.6

2012 2.6 4.8 3.6 6.0 2.9 5.3 5.5 4.7 9.1 1.2 12.7 11.2 8.3

2013 2.7 5.0 3.8 7.3 3.0 5.2 5.3 4.2 9.2 1.3 11.2 10.7 8.2

2014 2.7 4.9 3.5 7.8 2.9 4.9 5.2 4.4 9.1 1.4 10.9 10.9 8.4

2015 2.5 5.3 3.2 6.7 2.7 4.8 5.1 8.3 9.0 1.3 10.3 11.1 8.6

2016 2.5 5.3 3.0 5.6 – 4.7 5.0 7.8 8.7 1.3 9.8 11.2 9.0

2017 2.4 5.6 2.9 5.6 5.3 4.7 5.5 7.4 8.6 1.5 12.8 11.2 8.9

48 E. Ozen and S. Grima

4 The Turkish Life Insurance Market: An Evaluation of the Current Situation and. . .

49

Moreover, as shown in Table 4.1, the insurance penetration rate is lowest in Turkey (1.5), Lithuania (1.9), Greece (2.0), Latvia (2.1) and Mexico (2.2), with Turkey being the country with the lowest penetration rate among OECD countries. Between the periods 2012 and 2015, the share of life insurance in Japan was 100% of the total premiums collected in this country. Among other OECD countries, Australia, Belgium, Chile, Denmark, Finland, France, Greece, Ireland, Israel, Italy, Korea, Luxembourg, Norway, Portugal, Sweden and the United Kingdom have over 50% share of the life insurance premium in the total insurance premium. Turkey (17.3%), Iceland (8.8%) and the Netherlands (19.6%) have less than 20% share of life insurance premium in the total amount of collected premiums (Table 4.2).

4.3

Life Insurance Market in Turkey

At the end of 2018, there were 39 registered non-life insurance companies, 23 life insurance companies and 3 reinsurance companies in Turkey. While the number of employees in insurance and pension companies was 16,007 in 2008, it decreased to 10,652 in 2010 with a decrease of 33.45% due to the impact of the global financial crisis. As the effects of the crisis gradually disappeared, the number of employees increased back to 14,062 in 2018. However, the number of employees has not recovered back to the 2008 amount (IAT N.D.). Figure 4.2 shows the premium amounts in life and non-life insurance branches during the period between 2008 and 2018. It is observed that both the life insurance and non-life insurance premiums are very volatile. Life insurance premiums increased by 18.3% over the last 11 years from USD 1209 million to USD 1430 million. On the other hand, life premium production which was 1875 million USD in 2017 decreased by 23.73% in 2018. One of the most important reasons for this is that life insurance customers are individuals who purchase it for home loans (Swiss Re 2019). Besides, in 2018, the amount of premiums in non-life insurance branch decreased by 9.57% compared to the value recorded in 2017. Non-life insurance premium decreased by 10.05% over the examined period. As a result, total premiums decreased by 6.26% to USD 8469 million. It can be said that one of the main reasons for this fluctuation is the high volatility in exchange rates during the period studied. Figure 4.3 shows the distribution of premiums in life and non-life insurance in Turkey in 2018. The largest share of insurance premiums 29% is generated from motor vehicle third-party liability insurance which is mainly compulsory; then ranking second in terms of premiums is land vehicles insurance (Motor Own Damage) at 14.35%; fire and other perils insurance stands in the third place at 12.76%; and life insurance premiums with 12.66% stands in the fourth place. In fact the share of life insurance declined to the lowest level in the last decade. Table 4.3 lists the 23 companies in the life insurance sector and their market shares. “Ziraat Hayat ve Emeklilik AŞ” alone accounts for approximately 17.99% of

Country Australia Austria Belgium Canada Chile Czech Republic Denmark Estonia Finland France Germany Greece Hungary Iceland Ireland Israel Italy Japan Korea Latvia Lithuania Luxembourg Mexico Netherlands

Year 2007 % 61.9 40.8 70.6 46.0 – 40.7 65.5 – 45.9 59.8 41.8 48.4 54.9 8.0 83.6 48.3 61.9 76.9 66.3 – – 89.4 43.6 56.1

2008 % 59.4 40.2 66.7 33.9 – 40.7 67.5 – 45.3 64.4 42.1 47.1 52.5 7.3 79.6 50.0 59.2 79.6 63.8 – – 89.8 44.6 54.6

2009 % 55.9 – 64.8 35.6 65.1 41.8 – – 49.1 16.9 46.2 46.0 50.3 9.5 80.9 49.1 69.0 80.0 62.0 15.9 – 93.2 44.0 50.8

2010 % 57.0 – 65.0 35.6 66.0 46.0 – 40.6 59.5 17.2 47.9 43.3 53.2 7.5 80.3 51.6 71.8 80.2 59.6 23.0 34.7 94.3 46.1 51.1

2011 % 56,7 – 63.2 34.0 62.5 46.4 68.2 39.8 51.2 63.9 45.7 43.7 54.6 7.3 79.2 52.8 67.1 80.7 57.5 18.4 33.0 90.4 44.7 51.4

Table 4.2 Life insurance share of total insurance premium in OECD countries 2012 % 53.6 – 64.7 32.1 65.5 46.9 68.7 33.9 51.2 62.1 35.5 43.2 53.0 7.3 80.3 52.9 66.5 100.0 59.0 17.6 32.0 93.4 46.2 47.6

2013 % 54.9 34.1 57.7 32.9 66.5 45.7 95.4 29.3 56.8 62.4 36.0 41.4 54.5 7.9 80.6 52.9 71.4 100.0 59.3 18.9 32.0 91.9 46.1 47.4

2014 % 61.1 35.5 56.5 28.7 65.8 45.1 67.7 30.3 57.4 64.2 35.9 49.5 54.8 8.2 82.2 51.7 76.8 100.0 56.8 20.6 35.8 93.9 46.8 47.7

2015 % 59.2 35.3 55.4 28.7 68.3 41.1 68.9 31.2 58.3 64.2 34.0 50.5 51.1 8.4 79.6 53.6 77.8 100.0 57.1 21.6 36.6 92.3 46.3 43.6

2016 % 55.4 32.5 54.4 – 70.4 40.6 68.7 30.0 50.6 58.0 33.1 47.2 49.5 8.8 68.5 52.7 75.8 75.5 56.3 23.2 34.7 91.5 46.7 19.6

2017 % 52.1 31.7 53.7 – 69.4 38.3 69.1 27.3 50.7 55.2 32.1 50.4 48.2 8.9 82.5 53.8 75.0 75.1 – 22.5 29.2 91.8 43.0 18.2

50 E. Ozen and S. Grima

61.0 58.3 67.4 49.6 21.8 40.8 49.8 52.6 12.2 83.2 39.1 53.8

58.7 65.7 71.0 52.3 29.3 44.0 46.7 51.2 13.4 76.3 39.8 52.4

– 58.9 70.8 51.5 21.9 47.6 50.8 47.7 17.0 76.0 39.4 49.2 53.6 58.0 74.6 53.0 22.4 47.6 48.8 47.4 17.5 73.6 39.3 49.6

Source: Adapted from Insurance Association of Turkey (N.D.)

Norway Poland Portugal Slovak Republic Slovenia Spain Sweden Switzerland Turkey United Kingdom United States OECD
Total

53.7 55.7 64.2 53.2 22.0 46.9 49.6 48.4 18.3 73.5 40.1 52.9

54.6 58.1 63.3 53.9 22.1 46.4 54.0 47.9 15.4 75.4 40.3 53.2

54.2 54.0 70.5 55.5 22.4 44.6 52.2 48.2 15.7 72.4 38.5 52.7

55.3 52.2 72.9 54.3 22.9 43.9 69.6 48.5 12.3 73.4 36.2 50.2

55.1 50.2 67.5 51.2 23.1 43.6 73.3 48.9 11.8 73.3 37.1 49.2

55.4 42.6 59.6 – 22.5 47.1 74.9 46.9 14.9 68.8 35.9 46.8

55.4 39.4 58.6 73.6 22.4 44.8 76.1 45.0 17.3 68.3 35.6 45.3

4 The Turkish Life Insurance Market: An Evaluation of the Current Situation and. . . 51

52

E. Ozen and S. Grima 12,000

Premiums-USD

10,000 8,000

10,736

9,230

9,035 7,826

8,007

6,834

7,210 5,762

6,000

7,997

7,870

10,191

9,777

9,085 7,588

6,270

8,457 8,525 7,076

9,659 7,784

8,469 7,039

4,000 2,000 1,209

1,173

1,448

1,600

1,505

1,780

1,497

1,381

1,667

1,875

1,430

0 2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

Years Life Insurance

Non-life Insurance

Total

Fig. 4.2 Life and non-life insurance premiums in Turkey (Million USD). Source: Adapted from Insurance Association of Turkey (N.D)

the total life premium and the share of the top ten companies accounts for 84.24% of the total premium.

4.4

Factors Affecting the Insurance Market in Turkey

Overall in Turkey, individuals usually purchase insurance because it is mandatory by law and are hesitant to take on insurance cover for other reasons. Therefore the demand for optional life insurance remains low. Moreover, the social and economic crisis experienced in Turkey during the past years reduced the purchasing power of individuals and hence the demand for insurance cover (Baştürk and Sayın 2009). However, banks also affect life insurance premiums, since they offer and oblige individuals taking house loans to take out life insurance cover (Saka 2017). In this case, life insurance premiums may change depending on the take-up of house loans. Table 4.4 shows the size of the funds in house loans and life insurance. The housing loans pool, which was USD 29.9 billion in 2008, reached USD 38.9 billion in 2018. The life insurance premiums increased from USD 1.2 billion to USD 1.4 billion in 2018. The table shows a linear relationship between the two series. Figure 4.4 provides a better picture of the strong relationship between the house loans and life insurance premium. However, although the house loans decreased in the last 2 years, life insurance premiums increased, maybe due to an increase in the value of the premiums charged. Regression analysis was conducted to determine the link between house loans and life insurance premiums and to understand the impact of house loans on life insurance. In Table 4.5 we show that house loans account for 89% of the change in life insurance premiums (R2 ¼ 0.89, F (1.10) ¼ 80.932, p < 0.001). Therefore, almost all life insurance contracts result because of house loans.

4 The Turkish Life Insurance Market: An Evaluation of the Current Situation and. . .

53

LEGAL PROTECTION 0%

BOND INSURANCE 0% ASSISTANCE FINANCIAL 0% CREDIT LINES 1% 0%

ACCIDENT 3% LIFE 13%

GENERAL MARINE LIABILITY LIABILITY, 3% 0.06% AVIATION LIABILITY, 0.36%

HEALTH AND SICKNESS 11%

LAND VEHICLES (Motor Own Damage) 14%

ROLLING STOCK 0%

MOTOR VEHICLE COMPULSORY THIRD PARTY LIABILITY 29%

AVIATION 0% FIRE AND OTHER PERILS 13%

MISCELLENAOUS 10%

SHIPS HULL AND MACHINERY 1%

MARINE GOODS IN TRANSIT 2%

Fig. 4.3 Turkey: Life and main non-life % of insurance premium shares by type of contract in 2018. Source: Adapted from Insurance Association of Turkey (N.D)

However, according to Firat (2016), education, traditions, religion, law and justice, folklore, ethics, taboos, world view, values and attitudes, which are part of culture, can play a role in the individuals’ financial decisions. The concept of trust is a feeling that occurs in an individual as a result of social experiences. As a result of positive or negative events in the past, individuals may have high or low level of trust in their environment. The individual’s trust also affects his/her decisions. He notes that politicians, bureaucrats and the man in the streets in Turkey do not understand the value of the insurance and its functions. This and the technical interest rate applied to the savings of insurers which was less than the inflation rate has lessened the confidence in insurance, More so since the share of profits was

54

E. Ozen and S. Grima

Table 4.3 Turkey main life insurance companies in 2018 Rank 1 2 3 4 5 6 7 8 9

Company name Ziraat Hayat ve Emeklilik AŞ Anadolu Hayat Emeklilik AŞ Allianz Yaşam ve Emeklilik AŞ MetLife Emeklilik ve Hayat AŞ Aegon Emeklilik ve Hayat AŞ AvivaSA Emeklilik ve Hayat AŞ Garanti Emeklilik ve Hayat AŞ Halk Hayat ve Emeklilik AŞ Cigna Finans Emeklilik ve Hayat AŞ Vakıf Emeklilik ve Hayat AŞ First 10 companies Other 13 companies Market total

10

Total written premium (USD Million) 257 132 123 122 105 101 100 92 88

Share (%) 17.99 9.23 8.58 8.51 7.33 7.08 6.99 6.44 6.14

85 1,205 225 1,430

5.94 84.24 15.76 100.00

Source: Adapted from Insurance Association of Turkey (N.D) Table 4.4 House loans and life insurance premium pool during a period in Turkey (billion USD) Years 2008–2012 2009–2012 2010–2012 2011–2012 2012–2012 2013–2012

Loans 29.9 29.0 40.5 44.7 47.8 58.2

Premiums 1.2 1.2 1.4 1.6 1.5 1.8

Years 2014–2012 2015–2012 2016–2012 2017–2012 2018–2012

Loans 57.1 52.5 54.2 52.6 38.9

Premiums 1.5 1.4 1.7 1.9 1.4

Source: Adapted from Banking Regulation and Supervision Agency (2019) 250

Index Value

200

150 100 50 0 2008-12 2009-12 2010-12 2011-12 2012-12 2013-12 2014-12 2015-12 2016-12 2017-12 2018-12

Years Loan Index

Premium Index

Fig. 4.4 House loans and life insurance premiums index by years in Turkey. Source: Adapted from Banking Regulation and Supervision Agency (2019)

4 The Turkish Life Insurance Market: An Evaluation of the Current Situation and. . .

55

Table 4.5 Regression analysis results Model summary Model R R square Adjusted R square 1 0.943a 0.890 0.879 (a) Predictors: (Constant), Housing loans volume ANOVAa Model Sum of squares df 1 Regression 43.655 1 Residual 5.394 10 Total 49.049 11

Std. error of the estimate 0.73444

Mean square 43.655 0.539

F 80.932

Sig. 0.000b

Source: Authors’ compilations a Dependent variable: Life insurance premium b Predictors: (Constant), Housing loans volume

not fully explained to the prospective insured and profit-sharing in life insurance is confused with receivables from a bank deposit account. He notes that other detriments to the take-up of life insurance are the religious beliefs that it is a gamble and an unfair profit making business, the effect of inflation and fluctuation in currency exchange, the legal system which was not timely in the development of regulations in the field of life insurance and the so-called reinsurance monopoly since Turkish insurance companies transfer all of the premiums they collect through foreign reinsurance. This has had a significant impact on the development of national insurance and on the structure of the country’s insurance sector. Increasing confidence in financial institutions increases the frequency and amount of financial transactions (Boz and Ozen 2019). Accordingly, the level of trust of individuals in the insurance system, rules and insurance companies will have an impact on their insurance demand (Ozen and Grima 2018). Figure 4.5 shows the social trust rating of individuals by countries and Turkey features as one of the lowest. This has an effect on life insurance premiums since studies found that the level of social trust of individuals is correlated to life insurance premium production (Scicluna et al. 2019; Zammit et al. 2018). It can be said that there are four main factors that affect the development of the insurance sector. These are (1) legal infrastructure; (2) companies’ success in providing insurance services; (3) economic conditions; and (4) psychological sociological structure of the individual and society. It is seen that the regulatory authorities in the insurance sector take decisions about the functioning of the system and turn to the solution of problems encountered in practice (Scicluna et al. 2019; Zammit et al. 2018). Regulatory authorities should make the necessary legal arrangements in order to reduce the bureaucracy in insurance transactions and to take the necessary decisions in a timely manner to solve the problems. It is important for the system that insurance companies produce products that meet the needs of customers, give importance to customer relations and solve customer complaints in a timely manner by creating effective communication

56

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Fig. 4.5 Social/interpersonal trust for selected countries. Source: Adapted from World Values Survey (2014)

channels. Individuals who are not satisfied with the insurance services are less likely to renew insurance policies. Economic conditions are another important factor affecting insurance premiums. Factors such as income growth, education increase and urbanisation affect the premium production (Outreville 1996). On the other hand, Ozen and Grima (2018) acknowledged that economic factors had a positive impact on insurance demand, but found that individuals did not increase their insurance demand despite clear risks. Among other factors, certain characteristics of the individual and society have a significant impact on premium production. Individuals’ knowledge about insurance, low level of awareness (Firat 2016), differences in social and cultural characteristics of countries (Cristea and Tufan 2006) and demographic and personal characteristics of individuals affect attitude and insurance policy purchase decisions (Aydin and Koç 2016). Ozen and Grima (2018) demonstrated the importance of equal application of laws in the formation of insurance claims. In addition to the cultural characteristics of the society and the individual, the inability to protect the rights of individuals in the insurance policy and insufficient benefit obtained in return for paid premiums lead to a decrease in the sense of justice. Negative experiences adversely affect individuals’ trust in the insurance system. The feeling of distrust in individuals can often be more decisive on a purchase decision than other factors.

4.5

Conclusions

Onen (1992) highlighted the remarkable fact that life insurance production in Turkey first grows like a snowball and then melts. Life insurance is a tool that plays an important role in the solution of many uncertainties about the future and that arise in

4 The Turkish Life Insurance Market: An Evaluation of the Current Situation and. . .

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our modern life. Therefore it can be a solution to this observed vulnerability and can be the answer to the country needs to establish ways in which to stabilise and ensure steady growth. Insurance penetration in Turkey in 2018 averages 1.33%, which is seven times lower than the average of OECD countries (i.e. 8.9%) and that of other European Union (EU) countries, where the ratio of total life insurance premiums is around 58%, while the same rate is under 15% in Turkey. On the other hand, the share of life insurance premiums in 2018 is 17.3% of the total insurance premiums collected, ranking second from the last of OECD countries in terms of premium. Findings highlight that in the main individuals demand insurance only because it is compulsory and the trend is that house loans and social trust play a very important role in the production of the current life insurance premiums. Both having a positive impact on life insurance premiums, since the banks oblige individuals taking house loans to purchase life insurance cover, while more trust in the financial system will entice individuals to purchase more life insurance cover and vice versa. However, some reports/literature indicate insufficient awareness of insurance, the level of communication of insurance companies with insured people and satisfaction with these products and services can affect the take-up of life insurance cover. In order to increase the life insurance take-up companies in Turkey need to establish good communication channels between the insurance institution and the prospective customers. Good and appropriate training to the personnel in the insurance institutions can be one of the answers to achieve this goal. An increase in life insurance premiums can help trigger economic growth with the potential to generate an increase of funds in the Turkish economy and help individuals and businesses cover or transfer risks they do not want to carry. Therefore, understanding the factors and trends that impact the take-up of life insurance in Turkey and acting upon them will help stabilise economic growth and ensure sustainability of it over the coming years.

References Accessed November 27, 2019a., from http://www.sigortadunyasi.com.tr/2019/07/05/swiss-re2018-sigma-raporu-aciklandi-global-prim-uretimi-tarihte-ilk-kez-5-miyar-dolari-asti/ Accessed November 10, 2019b., from https://www.resmigazete.gov.tr/eskiler/2016/12/201612179.htm Aydin, G., & Koç, E. (2016). Social marketing analysis of attitude toward compulsory earthquake insurance in Turkey. Journal of Management & Economics, 23(2), 389–407. https://doi.org/10. 18657/yecbu.81769 Banking Regulation and Supervision Agency. (2019). https://www.bddk.org.tr/Veriler Baştürk, F. H., & Sayın, E. (2009, October). The effects of 2008 crisis on Turkish insurance industry. In International conference on finance and banking, Ostravice Czech Republic (Vol. 2829, No. 2009, pp. 38–57) Boz, H., & Ozen, E. (2019). The relationship between customers’ tendency to avoid risk and preferring online banking services. Gümüşhane University Electronic Journal of Social Sciences Institute, 10(1), 220–230.

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Cristea, M., & Tufan, E. (2006, May 5). Are there any differences in insurance behavior between two developing countries? Evidence from Romania and Turkey, economy and transformation management. In 3rd International conference, Timisiora, Romania. Firat, A. (2016). Insurance in Turkey and main problems of the sector. Communication in Mathematical Modeling and Applications, 1(2), 50–57. Insurance Association of Turkey (IAT). (N.D.) Accessed October 25, 2019, from https://www.tsb. org.tr/resmi-istatistikler.aspx?pageID¼909 Insurance Europe (IE). (2019). European insurance – Key factors. Available at https://www. insuranceeurope.eu/sites/default/files/attachments/European%20insurance%20%E2%80%94% 20Key%20facts.pdf OECD Statistics. (N.D.) Accessed November 10, 2019, from https://stats.oecd.org/Index.aspx? DataSetCode¼INSIND Onen, A. (1992). 62 year story of life insurances in our country. Reassure Journal, 5(p), 12–16. Outreville, J. F. (1996). Life insurance markets in developing countries. The Journal of Risk and Insurance, 63, 263–278. https://doi.org/10.2307/253745 Outreville, J. F. (2013). The relationship between insurance and economic development: 85 empirical papers for a review of the literature. Risk Management and Insurance Review, 16(1), 71–122. https://doi.org/10.1111/j.1540-6296.2012.01219.x Ozen, E., & Grima, S. (2018). Analysis of the influencing factors on the farmers’ take-up of greenhouse agricultural insurance cover: A case study. International Journal of Economics & Business Administration (IJEBA), 6(4), 14–33. Saka, E. (2017). Türkiye’de sigortacılık faaliyetleri ve bankasürans. Bankacılık ve Sermaye Piyasası Araştırmaları Dergisi, 1(2), 33–52. Scicluna, L., Seychell, S., Spiteri, J., & Grima, S. (2019). The Maltese financial services industry’s perception on the regulators: An empirical analysis. European Research Studies, 22(1), 16–51. Swiss Re Institute. (2019). https://www.swissre.com/dam/jcr:b8010432-3697-4a97-ad8b6cb6c0aece33/sigma3_2019_en.pdf Turkish Statistical Institute (Turkstat). (2019). Accessed November 10, 2019, from https://biruni. tuik.gov.tr/medas/?kn¼95&locale¼tr World Bank. (2019). Accessed November 10, 2019, https://data.worldbank.org/country/turkey World Values Survey. (2014). Accessed November 02, 2019, from https://ourworldindata.org/trust. CC BY. Zammit, M. L., Spiteri, J., & Grima, S. (2018). The development of the Maltese insurance industry: A comprehensive study. Bingley: Emerald.

Chapter 5

The Role Played by EIOPA in the Developments in the Insurance Sector European Consumer Protection Model Jan Monkiewicz and Marek Monkiewicz

5.1

Introductory Remarks

In the aftermath of the global financial crisis, financial consumer’s protection has become an important element of global regulatory debate, repair proposals, and policy initiatives. It started with an adoption by G20 in 2011 a first ever global document on financial consumer protection, recommended for global use. It contained ten important principles, including inter alia subjecting financial institutions and their agents to consumer protection in national legal, regulatory and supervisory systems, existence of special oversight bodies, public disclosure, and transparency as a key obligation of financial institutions, making equitable and fair treatment of consumers a part of the value and governance system of the financial institutions, the need for responsible business conduct of financial institutions, organization of accessibly internal and external redress systems, and finally, mandatory expansion by the state of financial education activities. These principles were of horizontal nature and supposed to be applied across all financial sectors, including insurance. European answer to consumer protection in insurance was predominantly located in the consumer’s protection agenda of EIOPA, which, by and large, grew to the dominant institutional player in this respect. Its program included a set of different initiatives, such as regulation of PRIIPs, development of strategy toward a comprehensive risk based and preventive framework for conduct of business supervision, introduction of product oversight and governance arrangements, elaboration of the framework for assessing conduct risk through the entire product life cycle, thematic

J. Monkiewicz Warsaw University of Technology, Warsaw, Poland M. Monkiewicz (*) University of Warsaw, Warsaw, Poland © Springer Nature Switzerland AG 2020 M. Borda et al. (eds.), Life Insurance in Europe, Financial and Monetary Policy Studies 50, https://doi.org/10.1007/978-3-030-49655-5_5

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review on monetary incentives between providers of asset management services and insurance undertakings, etc. The paper is split into three parts. In the first part, we set the global framework, where new regulatory paradigm of the financial system comes to the front. In the second part, we concentrate our attention on the analysis of the new developments in financial consumer protection as a part of the global agenda. Finally, in the third part, we focus our attention on the role and performance of the European Insurance and Occupational Pensions Authority in the protection of consumers in the insurance sector.

5.2

Elements of the New Global Regulatory Paradigm of the Financial System: Washington to Basel and Beyond1

For almost 30 years before the outbreak of the Global Financial Crisis in 2007, regulatory and supervisory paradigm of the financial system was based on the principles of the Washington consensus. The whole of this “Washington consensus,” invented and applied by IMF and surrounding institutions’ recommendations and policies, was based principally on the liberal economic thinking, primacy of privatization of the economy and financial systems, and efficient market orthodoxy. The leading institutions representing and advocating this paradigm included IMF, US Department of Treasury, The World Group, and OECD.2 Efficient market orthodoxy relied on full faith in the efficiency and rationality of the financial markets and thus pivotal role of the market discipline. It has been assumed that the financial markets are in principle efficient, and their proper functioning required basically only adequate access of the market participants to the market information and adequate operation of the market discipline. “Washington consensus” has been basically assuming that the financial system is safe with the private risk management, applied at the level of individual financial institutions. There was no need for supervisory authorities to intervene beforehand, ask questions, and take decisions, sometimes contrary to the thinking of the management. It believed consequently that financial innovations are generically good, providing more opportunities and competition in the financial systems and hence making them safer. It turned out later that this has not been true and that leading innovations at that time like securitization, derivatives, or risk management models that supposed to outplay the inefficiency of the market, brought finally detriment to the financial market. 1

First version of this discussion was presented in Monkiewicz J, Gąsiorkiewicz L, and Monkiewicz M. The changing architecture of the safety net in insurance worldwide: postcrisis developments, Economics and Business Review, vol. 1(15), nr 3, 2015, pp.6–8. 2 Helleiner and Bretton (2010).

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Table 5.1 Washington and Basel consensus in comparison Features Washington consensus

Basel consensus

View on financial markets • Largely efficient, rational and self-corrective • Prone to short-term disruption • Financial innovations contribute to financial stability and safety • Require better more timely information but should be left to their own devices • Corporate governance privately determined • Financial markets are inherently procyclical and prone to herding • Financial innovations and increasing complexity important destabilizing factors • Corporate governance and business models subject to public control

Instruments applied • First hand role of the market discipline • Enhanced transparency and disclosure • Private risk management (VaR models) within the financial institutions

Supervision in place • Formal and superficial • Micro focus • System made safe by allowing individual institutions to manage risk • Supervision isolated from politics

• Firsthand role of the regulatory discipline • Enhanced regulatory and supervisory powers • Public management of the systemic risk • Leverage limits and countercyclical capital buffers

• Material, penetrating and profound • Macrosystemwide perspective • Safety of the financial system becomes public preoccupation • Excessive complexity and financial innovations put under strict control • Supervision infiltrated by politics

Sources: Own elaboration and Baker (2013), 18(1), p. 117

“Washington consensus” focused its attention on the safety and stability of individual financial institutions. It believed that if individual entity is safe, the whole population must be by definition safe. This fault of fallacy produced later during the crisis unexpected negative results. Old consensus focused supervisory attention on microprudential authorities (Table 5.1). The focus of the new regulatory paradigm—“Basel consensus”—is clearly a “macroprudential” approach and with this a recommendation for the public management of the risks of the financial system. It has been basically developed by global regulatory bodies sitting foremostly in Basel at BIS premises. It included inter alia Basel Committee on Banking Supervision, International Association of Insurance Supervisors, and Financial Stability Board, working under the auspices of G20. The role of the latter institution was particular important as it was for the first time when new economic powers like China, Brazil, India, Malaysia, South Africa, Korea, Argentina, Russia, etc. were bringing their regulatory thinking and regulatory agenda to the center of the globe and replacing the views from the past. The upcoming new regulatory system is growing substantially in complexity in comparison with the one we know. It is evidently introducing a multilayer regulatory architecture and a wide array of new obligations vis-a-vis financial institutions.

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On the supervisory front, we are confronted with the development of the enhanced supervisory penetration and emergence of a multipolar supervisory systems. This includes introduction of the special regulatory standards for the systemically important financial institutions and giving special resolution powers to the public bodies when public interest is endangered. Special crisis management arrangements are becoming an important part of the new regulatory and supervisory framework. Their role is to limit potential negative spillovers and secure crisis management plans of action in advance to avoid improvisation. Finally the new enhanced role of the financial consumer protection, both in regulations and supervisory system, becomes advocated. Basel paradigm is since recently challenged by the views associated with the experience and success of new economic models represented by a group of states promoting state capitalism. It is China the first place with its excellent record of economic and financial development in the last 30 or so years. But it includes also Vietnam, India, and Russia placing the state and public champions in the center of the development. This Beijing consensus offers even more presence of the state in the economy and finance, comparing to Basel model.

5.3

Consumer Protection in Financial Sector: In Search of the New Approach

As indicated before, an important element of the new regulatory and supervisory paradigm of the financial markets refers to the financial consume’rs protection. The changes taking place were of fundamental nature, moving the whole area to the forefront of the new regulatory approach. It resulted from the observation that inadequate protection of the consumers may lead to the destabilization of the entire financial systems. Thus, the whole issue has been converted from the private to the public area.3 As underlined in the introduction to the G20 high-level principles on financial consumer protection, produced in 2011, “Consumer confidence and trust in a wellfunctioning market for financial services promotes financial stability, growth, efficiency and innovation over the long term. Traditional regulatory and supervisory frameworks adopted by oversight bodies contribute to the protection of consumerswhich is often and increasingly recognised as a major objective of these bodies together with financial stability. However and while it already exists in several jurisdictions, additional and strengthened dedicated and proportionate policy action to enhance financial consumer protection is also considered necessary to address recent and more structural developments.”4

3 4

Global survey on consumer... (2013). G20 (2011).

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Table 5.2 Old and new consumer consensus on financial services

Old consensus

New consensus

View on consumer 1. Economically rational in his behavior 2. Able to understand and select market offerings 3. Enhancing market discipline with his decisions

Instruments 1. Market disclosure and transparency 2. Market competition 3. Consumer policy largely private domain 4. Public policy focused on financial education

1. Consumers base their choices largely on non-economic factors 2. Consumers lack frequently competences to make their choices rational 3. Disciplining impact of consumers on the financial markets is strongly limited

1. Effective market disclosure and transparency controlled by administrative actions 2. Consumer policy transferred to the public domain 3. Conduct of business supported by product management 4. Financial education supported by financial institutions

Role of regulation and supervision 1. Regulations focused on insuring maximum market discloser and transparency 2. Absence of global protection standards 3. Reactive and formal supervision 1. Consumer regulations covered by public interest 2. Enhanced role of the global coordination of consumer protection standards 3. Enhanced pre-emptive supervision and enhanced market analytics

Source: Own elaboration

Theoretical framework for the financial consumer protection until the recent global financial crisis was unconditionally based on the orthodoxy of the rational choice. It assumed that consumers in their market behavior are in principle economically rational. They are able to evaluate all possible options and make rational choice. By making their choices, they were additionally inducing the financial institutions to take proper actions responding to their needs. To make it happen, it was just necessary to reach adequate level of disclosure and transparency of the financial institutions. The principal role of regulation and supervision under these assumptions was simply ensuring for the implementation of the conditions which provided for the active disclosure and transparency approach. It was accomplished, inter alia, by broad use of mandated disclosure systems affecting financial intermediaries and financial manufacturers. It was believed not only to lead this way directly to better financial consumer’s protection but additionally to enhance this goal indirectly via more active market competition among the financial institutions. Thus the provision of pre-contractual information was everything that was needed.5 Regulatory intervention into the consumer selection process or into the market offer was considered harmful and unnecessary (Table 5.2). The theory of rational choice assumed additionally that consumers are both willing and able to make their choices economically justified. To assist it, the 5

Pridgen (2013).

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provision of financial education by public authorities was recommended. It was believed to constitute an important part of the public consumer policy. The paradigm of the rational financial consumers was questioned on the practical front during the recent global financial crisis and on the theoretical front by the developments in behavioral economics.6 It turned out that real and not imaginary consumer is not acting economically rational when making his choices. Making his choices, he takes many noneconomic elements into his consideration and in particular those of psychological nature. Behavioral economics associates it either with his cognitive position and possible wrong interpretations or with his social attitude (e.g., herding) or his emotional approach like overconfidence and loss aversion.7 All these findings led to the conclusion that there is a need for the reconsideration of the existing approach and ideational vision of financial consumer. New approach is based on the assumption that there is no something like clever and rational partner to the financial transactions, at least among nonprofessional participants. It also questions the real usefulness of the transparency and disclosure as major consumer protection tool. Instead it calls for the active role of the regulator to participate in the consumer selection process, both via shaping market conduct principles as well as via influencing market offer. It should become a necessary element of the new financial stability network. New approach called additionally for the development of specialized consumer protection institutions which should become a part of the restructured holistic supervisory approach.

5.4

Changing Model of Consumers Protection in EU Insurance: From Product Centricity to Conduct of Business Rules

It should be noted that the issues of financial consumers protection, including insurance, have come to the forefront of EU institutions and EU programs relatively late. Effectively it was not before the establishment in 2011 of the three European Supervisory Authorities—European Banking Authority, European Insurance and Occupational Pensions Authority, and European Securities and Markets Authority. The new institutions received inter alia broad- based consumer protection duties and powers. It could be reasonably assumed that this development remained in some relation to the rising importance of financial consumers protection in the G20 postcrisis reform agenda. As far as insurance is concerned, the founding regulation of European Insurance and Occupational Pensions Supervisory Authority explicitly elaborated in its article 9 on the components of its tasks related to consumer protection.8 It says that the 6

Adenas and Chin (2014). Lefeuvre and Chapman (2017). 8 Regulation (EU) No 1094/2010 (2010). 7

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authority “shall take a leading role in promoting transparency, simplicity and fairness in the market for consumer financial products or services across the internal market.” It will do so by inter alia collecting, analyzing and reporting on consumer trends, reviewing and coordinating financial education initiatives, developing training standards for the industry, and contributing to the common disclosure rules. The Authority is also given the power to temporarily prohibit or restrict certain financial activities that threaten the proper functioning and integrity of financial markets and the stability of the financial system in the Union. Additionally it is obliged to establish a special internal body—Committee on financial innovations—to work out a coordinated approach to the regulatory and supervisory treatment of new or innovative activities and present relevant advice to the Parliament, the Council, and the Commission. Particular attention of EIOPA in its consumer protection activities was given at the initial period to the creation of appropriate conditions for the possibility of making by the consumer’s informed product choices. Thus transparency and disclosure seemed still to be in good price. The most debated and publicized element in this regard became an initiative related to the key information document tool for packaged retail and insurance-based investment products. Respective regulation was adopted already in 2014, but its final implementation, due to the complexity of regulatory area and its economic and political importance, was delayed until January 2018.9 Packaged retail investment and insurance-based products (PRIIPs) occupy central position in the European retail investment market. They offer many financial institutions which provide investment products dedicated to the savings aimed at achieving specific objectives such as house purchase, child’s education, pensions upgrading, etc. Insurers are among the active participants of the said market, apart from the banks, funds, and investment firms. The total value of the market is estimated by the EU Commission to reach currently the level of up to 10 trillion Euros. However, some of these products, despite of their potential benefits for investors, were complicated and lacking in transparency and were difficult for them to understand. The information provided by different institutions was often too complex and contained too much professional jargon. Additionally, the existence of different rules on PRIIPs that vary according to the specific industry offering the PRIIPs and differences in national regulations created an uneven playing field between different products and distribution channels. In order to tackle these shortcomings, the EU decided to adopt a regulation on PRIIPs which essentially obliged all those who produce or sell these products to provide retail investors with specially designed key information documents. The new regulation covered inter alia insurance-based investment products which are understood as “insurance products which offer a maturity or surrender value and

9

Regulation (EU) No 1286/2014 (2014).

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where the maturity or surrender value is wholly or partially exposed, directly or indirectly to market fluctuations.”10 The said regulation excluded expressiss verbis all nonlife insurance products, and these life insurance contracts where the benefits are payable only on death or in respect of incapacity due to injury, sickness, or infirmity. Key information document should constitute pre-contractual information and should be prepared by PRIIP manufacturer before the product is available to retail investors. The regulation requires that it should be clear, accurate, fair, and not misleading. It should be also concise, maximum size is of three sides of A4-sized paper when printed. The regulation specifies in a detailed manner type of information which should be contained in the document. It specifies that it should start with the name of the product and the details of the manufacturer. Thereafter it should define the type of retail investor to whom the product is intended. In case it is offering insurance benefits, it should additionally specify their details, including their triggers. Large section of the document is dedicated to risk and reward profile of the product. It includes a summary of risk indicator, supplemented by a narrative explanation. It contains indication of possible maximum loss of invested capital, including the information whether the retail investor can lose all invested capital. The document provides also an information on the costs investors have to bear when investing in the product and on potential consequences of cashing in before the end of the term period, such as the loss of capital protection or additional contingent fees. Additionally the document should include the information about how and to whom an investor can make a complaint about the product, its manufacturer, or advisor. All information contained in KID should be regularly reviewed and if necessary revised. It is interesting to note that with regard to insurance-based investment products, EIOPA is also given specifically the task of market monitoring and product intervention powers. They include temporary prohibition or restriction of product marketing, distribution, or sales. These could take place when the proposed action addresses a significant investor protection concern or constitute a threat to the stability of the financial system in the Union. If the issue is of a local nature, these powers should be executed by the competent national authority. The regulation foresees heavy administrative penalties in case of its infringement. The competent authorities may impose an order prohibiting the marketing of PRIIP, an order suspending the marketing of PRIIP, a public warning, and an order prohibiting the provision of a key information document. Additionally they are given the right to impose administrative fines which may come up to 5 million Euros for legal entity and 700,000 Euros for natural person.

10

Op. cit, art 4.

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Regulation on PRIIPs was de facto the first attempt of product centric customer protection initiative in the European life insurance so far and has been complemented in 2017 by a regulation on insurance product information document which covered all nonlife insurance products and which copied some of its ideas.11 An important new consecutive step in the development of the consumer-centric approach toward insurance products took place with the initiative of the three ESAs on product oversight and its governance system (POG arrangement) and its adoption in 2016.12 It is based on the solutions already applied and tested in the UK by the Financial Conduct Authority. The intention of the said regulation is strengthening of consumer protection by enhancing the consumer-centric controls of the products in their entire life cycle and in entire spectrum of distribution channels. The preparatory guidelines on product oversight and governance system require all insurance manufacturers and distributors to follow certain rules and procedures regarding product administration and distribution. The principal objective of this systemic solution is prevention and mitigation of customer detriment, elimination of conflict of interests, and support for the consumer protection. The guidelines are split into two chapters. Chapter 1 of the preparatory guidelines, composed of 12 rules, focuses on insurance undertakings and intermediaries who manufacture life and nonlife products for sale to customers. It obliges all insurance manufacturers to establish product oversight and governance system. It should cover designing, monitoring, reviewing, and distributing products for customers and taking actions in case of their detriment. They should reflect the system in a written document and make available to relevant staff. It should be endorsed by the management of the manufacturers which should be ultimately responsible for its proper operation. Additionally, in Chap. 1, several obligations of the manufacturers were specified including identification of the target market of the product, selection of the appropriate distribution channel, regular review of the POG Arrangements, and obligatory product testing before bringing them to the market, including if necessary a scenario analysis. It also included a duty of monitoring on an ongoing basis that the product remains aligned with the interests of the target customers. The manufacturer was also seen responsible for the provision of adequate information to distributors of the product. Chapter 2 of the Preparatory Guidelines, composed of nine rules, is addressing insurance distributors which distribute the product they do not manufacture. Here the guidelines require the distributors to establish product distribution arrangements and endorse them by the management bodies. It also requires the distributors to fulfill a number of obligations, including obtaining necessary information on products from the manufacturers, ensuring regular review of the product distribution arrangements,

11 12

Commission Implementing Regulation (EU) 2017/1469 (2017) Final report on public consultation on. . . (2016).

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and informing the manufacturer in case of inconsistency of the product with the target market. Most essential elements of the said Guidelines were subsequently included in the Commission delegated regulation on product oversight and governance requirements for insurance undertakings and insurance distributors, adopted in September 21, 2017.13 The aforementioned regulation was finally supplementing newly adopted insurance distribution directive and specifying its terms. Wrapping out our discussion, it should be emphasized that the said Guidelines and subsequent delegated regulations represented a fundamental shift in consumer protection paradigm in the EU. It was no longer based on the dominance of the disclosure principle and the responsibility of the customer who undertakes the decisions based on the market information he receives. Instead it becomes too much degree a responsibility of product manufacturers and distributors which should develop the supply of insurance products which should adequately address the needs of the customers. It is their responsibility to organize proper product offer and identification of adequate target market. It is also their responsibility to provide necessary information for the customers and taking care for its proper understanding. A further subsequent important development in the consumer protection area was an adoption by EIOPA of a document providing a comprehensive framework for conduct of business supervision.14 In its policy section part, the document underlines a need for a more consumer-centric culture in firms and the need of senior management to take more responsibility to prevent poor product oversight and misaligned incentives for sale staff. It also stresses that traditional approaches to conduct of business regulation and supervision focusing on disclosure and selling practices are insufficient. Additionally it indicates to the existence of the interrelationship between conduct and prudential issues, and thus the fact that poor conduct of business may contribute to the development of systemic risk. It clarifies also that the final aim of such conduct of business supervision is to avoid or become early enough aware of consumer detriment to act. In its descriptive part, the document advocates the need for the new supervisory approach toward consumer protection which should be based on a two pronged approach: risk based and preventive. It further explains that the risk-based approach means allocating priorities and resources according to the depth and scale of issues. Preventive approach means on the other hand anticipation of consumer detriment early instead of reacting following the emergence of the problems. In such approach market monitoring, data analytics and their intelligent use are most crucial. To reach its goals in this respect, EIOPA envisages a holistic approach composed of several complementary elements to be in place and be continuously developed. It embraces first of all deep and effective market monitoring both for general purposes and product intervention powers.

13 14

Commission Delegated Regulation (EU) of 21 September 2017. . . (2017). EIOPA’s strategy toward. . . (2016).

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An effective instrument of such monitoring are consumer trend reports and ad hoc surveys which are a valuable tool in detecting trends in consumer detriment and emerging risks. These consumer trend reports, issued annually already since 2012, are to be complemented by thematic reviews which are supposed target specific financial activity or a product of consumer concern. The first such fully blown thematic review, concentrated on monetary relations between asset managers and insurance undertakings in unit linked business, was completed in 2017.15 The motivation for launching this thematic review was, as explained by EIOPA, on one hand the market importance of insurance-based investment products and on the other hand the evidence of risks arising in regard to unit-linked products. As detailed by EIOPA assets of the unit-linked funds represented in 2015, about 40% of all assets held by life insurance undertakings within the EU (2, 5 trillions of Euro), and there were around 780 insurance companies pursuing unit-linked business in the EU. Therefore, it seemed highly justified to review how insurance undertakings address and mitigate the emerging conflict of interests and act in the best interest of customers. As an end product of the review EIOPA published subsequently its opinion on issues that were identified.16 It emphasized inter alia the need for monitoring monetary incentives received by insurance undertakings from asset managers by national competent authorities, and appropriate steps should be taken to prevent, identify, mitigate, and manage the resulting conflict of interest. It also requested from national competent authorities to provide guidance to insurance undertakings on possible arrangements to prevent conflict of interest, such as the rebating of monetary incentives received to policyholders. Additionally it demanded from insurance undertakings to disclose their monetary practices to their customers as well as provide the customers with appropriate information on the criteria used for the selection of funds on offer. Final element of the new supervisory approach is development of data gathering for its subsequent analytical activities. These include first of all a creation of the sound system of retail risk indicators which will assist pre-emptively the effects of product characteristics and distribution arrangements on consumer protection. It may cover such examples like claim ratios, combined ratios, surrender ratios, litigation development, etc. Solvency II reporting arrangements provide excellent opportunities in this regard. In its recent document, EIOPA confirmed its new supervisory approach toward conduct of business supervision and declared more initiatives in the coming years.17

Report on thematic review. . . (2017). Opinion on monetary incentives. . . (2017). 17 EIOPA’s strategy for conduct. . . (2018). 15 16

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Concluding Remarks

Twelve years after the outbreak of the recent global financial crisis and 7 years after G20 initiated a thorough regulatory review of financial consumer protection issues, it seems that European insurance sector comes slowly to grips with the retail policyholder protection. Its position evolved from fragmentary product-based approach, through product management and distribution arrangements toward finally holistic monitoring and supervisory framework. In each consecutive step, it departed more and more from traditional transparency and disclosure paradigm of the past into the direction of the responsible finance and consumer centricity. It raised substantially the role and responsibility of the insurance undertakings and distributors in the process of consumer protection. It represents today perhaps by far the most developed approach in the world. It is still very uneven, offering more regulation and protection for life segment than for nonlife, but it started to deliver.

References Adenas, M., & Chin, H.-J. (2014). The foundations and future of financial regulation. Governance and responsibility. Abingdon, NY: Routledge. Baker, A. (2013). The new political economy of the macroprudential ideational shift. New Political Economy, 18(1), 112–139. Commission implementing regulation (EU) 2017/1469 of 11 August 2017 laying down a standardised presentation format for the insurance product information document. Commission delegated regulation (EU) of 21 September 2017 supplementing Directive (EU) 2016/ 97 of the European Parliament and of the Council with regard to product oversight and governance requirements for insurance undertakings and insurance distributors. EIOPA’s strategy towards a comprehensive risk-based and preventive framework for conduct of business supervision, EIOPA-16/015, 11 January 2016. EIOPA’s strategy for conduct of business supervision-next steps, EIOPA-BoS-18-095, 23 April 2018. Final report on public consultation on preparatory guidelines on product oversight and governance arrangements by insurance undertakings and insurance distributors, EIOPA-BoS-16-071, 6 April 2016. Global survey on consumer protection and financial literacy: Oversight frameworks and practices in 114 economies, The World Bank, 2013. G20. High level principles on financial consumer protection. OECD, October 2011. Helleiner, E., & Bretton, A. (2010, May). Wood moment? In The 2007–2008 crisis and the future of global finance, International Affairs (pp. 619–6360). Lefeuvre, A. F., & Chapman, M. (2017). Behavioral economics and financial consumer protection. Paris: OECD. Opinion on monetary incentives and remuneration between providers of asset management services and insurance undertakings, EIOPA-BoS-17/295, 11 December 2017. Pridgen D. (2013). Sea changes in consumer financial protection: Stronger agency and stronger laws. SSRN. com/abstract¼2186035. Regulation (EU) No 1094/2010 of the European Parliament and of the Council of 24 November 2010.

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Regulation (EU) No 1286/2014 of the European Parliament and of the Council of 26 November on key information document for packaged retail and insurance-based investment products (PRIIPs), 2014. Report on thematic review on monetary incentives and remuneration between providers of asset management services and insurance undertakings, EIOPA-BoS-17-064, 26 April 2017.

Chapter 6

A New Model of Investment Life Insurance Distribution in the Context of Consumer Protection EU Policy Anna Ostrowska-Dankiewicz

6.1

Introduction

In recent years, in most European countries, there has been an intensive discussion regarding compliance with the correct rules for selling life insurance and ensuring increased protection of the insurance-based investment products1 market for consumers. Insurance activities and phenomena occurring within the scope of offering life insurance connected with the possibility of investing are subject to many perturbations, which are mainly associated with the increasing awareness of consumers. This is the result of the changes related to the application of the new regulatory and supervisory paradigm in the entire financial sector, which in the insurance industry influences the application of a new approach to problems and a model of consumer protection policy, as well as new models and standards of insurance distribution to ensure better protection for clients of insurance companies. The growing problem of a decrease in customer confidence in product distributors was a reason for taking actions and striving for a new model of consumer protection policy and the introduction of new rules in the field of life insurance distribution. This phenomenon results primarily from the use of unfair market practices by insurance companies, the policy of not informing customers, as well as the opaque design of the products offered. Ultimately, this led to massive abandonment of policies, the collapse of the investment policy market, and the weaker position of insurers. The new regulatory and supervisory paradigm of the financial market, which enforces the protection of consumers of innovative financial products, including 1 Insurance-based investment product (IBIP) means an insurance product which offers a maturity or surrender value and where that maturity or surrender value is wholly or partially exposed, directly or indirectly, to market fluctuations (Directive EU 2016/97).

A. Ostrowska-Dankiewicz (*) Rzeszow University of Technology, Rzeszów, Poland © Springer Nature Switzerland AG 2020 M. Borda et al. (eds.), Life Insurance in Europe, Financial and Monetary Policy Studies 50, https://doi.org/10.1007/978-3-030-49655-5_6

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insurance products, has thus become an opportunity to increase the safety of buyers of life insurance policies. As a result of the emergence of independent and separate supervision, the approach to protecting consumer rights on the insurance market has also gained importance. It manifests itself mainly through the use of pro-consumer measures specified by the supervising institutions regarding the reduction of information deficit in the applied distribution policy and the adopted new sales models of insurers.

6.2 6.2.1

Main Trends and Problems in the European Insurance-Based Investment Product Market Product Transparency Deficit

The functioning of the insurance-based investment product market in recent years, especially after the crisis on the financial market, is characterized by a decrease in clients’ confidence in insurers and especially an increase in the dissatisfaction of clients with investment policies. The reasons are primarily seen in the lack of transparency of this type of product. This lack of information transparency of the entire financial system results from the fact that many types of risk are “hidden” outside the balance sheets of certain financial institutions, including insurance companies. The problem was already pointed out during the financial crisis, noting the need to radically increase the transparency of the entire financial system (Fisher 2008). Considering the insurance market, the lack of product transparency is a particularly important problem, especially since life insurance products of a strictly investment nature dominate in sales on the markets of the member states and most often are exposed to limited information transparency, which is especially dangerous for the customer, especially since these products are characterized by high complexity and considerable complexity. The reasons for this should be seen in the consumer risk related to the insurance market, resulting from an imbalance between the parties to the transaction and the weaker position of the consumer in relation to the financial service provider. The explanation of this imbalance is on the one hand irrational behavior of consumers and on the other hand irregularities on the side of insurance providers.

6.2.2

Unfair Practices of Insurance Companies

All such activities of insurance providers that rely on the use of advantage over the customer are considered unfair and unlawful. Due to the variety of prohibited activities, the catalog of unfair practices introduced a transparent division into

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misleading activities based on false information and practices having the ability to build a misleading image of the addressee about market relations or the subject of the offer. This resulted in the emergence of a new concept of mis-selling – unfair selling. This term was first used in Great Britain, where it appears in the legal system as a serious violation of market principles and is sanctioned. Its first manifestations were recorded as early as in the 1990s, while the largest mis-selling scandal concerned payment protection insurance (PPI), the essence of which was to secure the borrower’s ability to repay loans and was that the insurance’s usefulness for the client was often very little or none. These insurances were accused of too high a price and a structure that was designed to limit payments to people who were really sick and to unfair sale to people who could not claim payment of benefits. By illustrating the scale of the incident, it is estimated that the number of consumers affected is 12 million, but a total of 64 million PPI policies have been sold, so the scale of the problem may be larger. The compensation paid for damages to consumers in this case amounted to GBP 24.2 million. In addition, PPIs were often sold to customers who could not later apply for a policy payment and were often unaware that PPI (The Price of Bad Advice 2018) was being sold to them. Another blatant example of mis-selling concerned insurance-based investment products sold in the Netherlands. It is estimated that this incident could affect one million consumers, although products that were too complex and opaque were sold seven million and consumer damage was valued at EUR 20–30 billion. They resulted mainly from the fact that excessive fees were charged to customers, and a significant part of the amount paid was not invested but covered administrative costs, commissions, and bonuses. Therefore, consumers did not know what part of the premium was invested or the actual cost of the product, which was the result of improper practices at the stage of selling the product. In the literature, unsafe and unprofitable insurance services are associated with the sale of products by misleading customers, improper sale, or sale of services unnecessary to the customer, which can cause a serious financial loss (Muller et al. 2014), especially since the most commonly used business models, defective sales patterns, and remuneration models lead to offering services to customers who don’t need them or can’t afford them. Insurers’ use of controversial sales behavior was divided into practices concerning (Christofilou 2014): • Cross-selling, which boils down to the provision of insurance services together with banking or investment services, and in practice the most often so-called basic product is usually a current account, mortgage, insurance, or investment program for which the additional service is sold • Tying practices, understood as offering or selling an insurance product along with other products, where the insurance product is not available to the customer separately • Bundling practices, under which insurance products can also be offered to customers as separate products, but not necessarily under the same conditions

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as in the case of an offer when the insurance product is bundled with other products. The phenomenon of misleading customers is most often the result of actions of financial entities aimed at increasing their financial efficiency. In turn, such activities are often accompanied by various types of manipulation, fraud, and unfair commercial practices, which are a consequence of unrealistic sales plans imposed on employees and inadequate incentive systems that increase employee pressure and encourage them to engage in inappropriate behavior (Czechowska and Waliszewski 2018). Such practices usually involve providing consumers with unreliable, incomplete, and sometimes even false information. From the point of view of insurancebased investment products, especially unit and index-linked as part of mis-selling, the most common are issues related to the lack of sufficient and clear information that at the end of the policy, a deficit may occur; the lack of a complete financial assessment of the consumer’s financial advisers, appropriate to the degree of investment risk of the product; and lack of consumer awareness that investment income will be possible only when the investment period provided for in the plan ends, advising sellers to invest in the policy under the guise of selling another financial product (Kościelniak 2016).

6.2.3

Cancellation of Policies and Lack of Confidence in the Financial Sector

Another problem that mainly concerns unit-linked savings is the massive wave of criticism from customers who are prematurely giving up policies purchased. The main objections toward these insurances include very high liquidation fees, especially in the event of termination of the insurance contract in the first 2 years of its duration. High liquidation fees mean that by resigning from the policy shortly after signing the contract, the cash value of the insurance is very strongly reduced in relation to the amount that can be obtained by multiplying the number of participation units by their value. It has been assumed in the literature that the purchase of unit-linked insurance is motivated by long-term goals; therefore the policyholder does not expect the policy to provide him with liquidity in the periods preceding the insurance maturity. According to this assumption, high liquidation fees should discourage early resignations and indicate that the market offers better products whose task is to meet the need for liquidity (Wiśniewski 2016). The issue of early resignation is confirmed by the hypothesis of financial stress, which suggests that they are the result of financial difficulties of the household, which were caused by income shock related to, e.g., loss of job or uninsured fortuitous events. In turn, the interest rate hypothesis assumes that giving up insurance with funds is the result of finding better forms of investing savings (Surrenders in the Life . . . 2012). Empirical studies have shown that the interest

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44 2012

47 2013

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Informed Public

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Mass Population

Fig. 6.1 Values of the confidence index to the financial sector in 2012–2019. Source: author’s own study based on: Edelman Trust Barometer (2019), Financial Services, Edelman Annual Global Study, p. 11

rate hypothesis better explains the motivation for giving up in unit-linked insurance, while the financial stress hypothesis better matches giving up in life and endowment insurance (Kiesenbauer 2011). It should be emphasized that there is empirical evidence that the issue of policy maintenance or cancellation depends on the level of consumer confidence and longterm interest rates for all insurance products tested. Similar applications were received for unit-linked insurance-based investment products. This underlines the importance of consumer confidence in financial service operators (Poufinas and Michaelide 2018), especially since the Edelman Trust Barometer survey published in 2019, which is an annual assessment of global trust, has shown that the financial sector still remains the least trusted industry. The latest edition of the study showed that the confidence index at the level meaning “lack of confidence in the financial sector” occurs in as many as 15 out of 26 markets studied. The lowest confidence index was recorded in European countries such as Germany, Italy, Spain, and Ireland (Edelman 2019). When examining trends in recent years, it should be pointed out that the confidence rate for financial services was by far the lowest in 2012, which was undoubtedly a derivative of the global financial crisis, but over the years the financial service sector has regained confidence to some extent. In the study (Edelman, p. 2), the observations of two groups of society were taken into account: on the one hand, the more informed persons with relatively high education, in the group of 25% of households with the highest income in a given age group on each market surveyed, representing 16% of the whole society, and, on the other hand, the answers of the mass public (general public), which includes the whole society excluding informed public, representing 84% of the global population; the level of confidence in the financial sector in 2012–2019 is presented in Fig. 6.1. Confidence in the financial sector was greater in the case of more educated people, among whom confidence in the financial sector was rebuilt already in

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2016, while in the case of the general public, it can be observed that they still do not fully trust financial services (the indicator is still oscillating at a level lower than 60). However, a positive tendency may be the fact that the value of the indicator in this case has broken the limit meaning a complete lack of confidence. In addition, higher confidence in the financial sector among better educated people suggests the importance of the role of financial knowledge and awareness in shaping the appropriate attitudes of the community and their confidence in the financial sector.

6.3

Structure of the Life Insurance Market and Consumer Complaints

Despite the fact that in the last 30 years the market of insurance-based investment products has been affected by a series of dangerous behaviors on the part of distributors, consisting mainly in misleading the customer by selling products that do not match the needs and expectations of customers who have resigned from waves at different times of insurance policies, this type of insurance is still quite popular. As for the situation in the European life insurance market, it should be emphasized that although the overall share of unit-linked insurance in the gross written premium in life insurance decreased from 43% in the fourth quarter of 2017 to 41% in the fourth quarter of 2018, this share remains relatively constant, and the median remains stable at around 34% (Financial Stability Report 2019). It should be emphasized that the focus on the profitability of insurers’ business operations still remains the main trend of their business, and continuous pressure to adapt to difficult market circumstances results in a reduction in guarantee rates and a focus on index-linked and unit-linked investment products (Fig. 6.2). Taking into account the share of gross written premium in individual life insurance on the markets of the European Union member states, it should be pointed out that, apart from typically investment products, insurers’ business lines focus also on insurance with profit share. Fig. 6.2 Gross written premium in life insurance by type of insurance in European Union countries. Source: European Insurance Overview (2018), EIOPA, Publications Office of the European Union, Luxembourg, https://doi. org/10.2854/168899, p. 8

2% 7% 8% 41%

10%

Index-linked and unit-linked insurance Insurance with profit participation Other life insurance Health insurance

32%

Life Reins HealthReins

6 A New Model of Investment Life Insurance Distribution in the Context of. . . Table 6.1 Customer complaints by the reason of submission

Complaint reason Sales Claims Conditions Commission Administration Others

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Share in general complaints (in%) 22 32 4 3 31 8

Source: Seventh Consumer. . . (2018)

However, considering individual EU member states, it can be seen that indexlinked and unit-linked insurance as well as profit-sharing insurance accounts for the majority of premiums written in almost all countries, with the exception of only Spain and Malta. Considering the market problems indicated, it is surprising that the total gross written premiums for selected life insurance business lines throughout the European Economic Area increased by 11%; in addition, the life insurance sector remains significantly larger than the property insurance sector, and in many member states, premium life insurance still amounts to more than 50% of the total premium written. The growth in the life insurance market was mainly due to a 42% increase in investment insurance premiums, which could be seen in most member states. There was also an increase in the number of complaints submitted by consumers to state supervisory authorities. According to the latest EIOPA data, complaints related to sales in 2017 increased by 100% compared to the previous year, although they were not the main reason for filing them, but they constituted 22% of the total number of complaints reported (Table 6.1), which was related to potential inappropriate sales (mis-selling) and low performance of some insurance-based investment products (Seventh Consumer . . . 2018). When analyzing the reasons for customers’ negative approach to specific products, resulting from both their design and low profitability, as well as the sales policy used by insurers, it should be emphasized that the problem of obtaining accurate information about the effectiveness of life insurance products is of particular importance to consumers. Especially in the face of growing threats resulting from the great difficulty in accessing information on these products (especially the scale of their emission and efficiency), it should be taken into account that this may affect further negative assessment of products by market participants, and in order to change the image of these products, measures should be implemented as soon as possible subordinated to new directions in consumer protection policy.

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Setting New Directions in the Field of Consumer Protection Policy in the Investment Life Insurance Market The Essence and Significance of the New Regulatory and Supervisory Paradigm

The reason for the start of intensive changes in regulatory standards and practices on the part of supervisors was the introduction of a new regulatory and supervisory paradigm in the financial sector as a consequence of the crisis of 2007–2010. Problems related to the proper policy of protecting consumers of the financial market, including the insurance market, were reflected in taking actions resulting from adapting to the new regulatory and supervisory paradigm, in which the so-called public risk management and ensuring security of the entire financial system, especially by increasing regulation (Committee . . . 2008). Greater importance was imposed on public intervention using protective regulations related to consumer rights in the financial market (Baker 2013). The starting point for the creation of the so-called the new Basel consensus was that the financial market was volatile and cyclical, with no guarantee of self-healing where innovation or herd behavior could destabilize the entire financial system. Such premises were adopted for conducting appropriate policy based on the intervention of public institutions in the form of a ban on the use of specific solutions or a ban or restriction of the sale of certain products. In addition, consumer protection, which is an important element of the supervisory system, including the supervision of insurance companies, has become a distinguishing feature of the Basel system, which was considered an important aspect within the information function of supervision. Separation of supervision in the area of consumer rights protection resulted in the establishment of such EU institutions as European Banking Authority (EBA), European Insurance and Occupational Pensions Authority (EIOPA), and European Securities and Markets Authority (ESMA). The key tasks of these entities are activities in the field of protecting the interests of consumers of financial services, including insurance, and creating pro-consumer policy in all European Union countries. The resulting financial market supervision system has implemented the application of macro-prudential principles. The Financial Services Action Plan gained considerable political support, which resulted in a European recovery plan to restore and maintain financial market stability (Kawiński 2015). Initiatives undertaken by the European Union led to the creation of the European System of Financial Supervision (ESFS), which was to involve national supervisors in its activities, for which the basic objectives are activities based on ensuring transparency, simplicity, an access to the insurance market, and integrity of its entities operating in relations with consumers according to the new model of consumer protection. It was pointed out that the implementation of the abovementioned objectives included, among others, creating conditions for consumers to make informed choices, creating a framework for proper sales practices and better management of product availability

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and suitability, as well as supporting the development of effective dispute resolution systems (Monkiewicz and Monkiewicz 2015).

6.4.2

Protective Solutions in Selected EU Legal Regulations

Actions aimed at improving consumer protection policy in the single EU life insurance market are taken cyclically. Regulations created at the EU level require implementation into the national law of the member states, which entails the risk of goldplating, i.e., creating more restrictive national law insurers than in the provisions of community regulations (Łańcucki 2015). From the point of view of rebuilding consumer confidence in the insurance market through effective regulation of sales processes of investment products, an important place was taken by EU regulations in the field of consumer protection focused on information. The Solvency II Directive (Directive 2009/138/EU) specifies exactly what information should be included in the contracts. In turn, the provisions of the Regulation of 26 November (Regulation 2014/1286) about the improvement of disclosed information quality regarding collective investment and insurance investment products (PRIIPs), a set of key information that should be provided to retail customers was defined. A rather revolutionary solution should be a protection model, which indicates that before any product enters the market, it must undergo an internal control process. Thanks to this, it is possible to determine for whom a given insurance is intended, what risks it entails, what costs and additional fees are associated with it, as well as who is to sell it and what is the possible dispute resolution system (Regulation 2016/1904). Undoubtedly, the most important among the recent acts of community law from the point of view of the discussed problems of the life insurance market is the so-called IDD (Insurance Distribution Directive), which priority is to protect the interests of the customer and ensure the distribution of products tailored to his needs (Directive EU 2016/97). The IDD entered into force on January 20, 2016, and repealed the older IMD (Directive EU 2002/92/EU) on insurance mediation. Changing the name of the directive reflects well the will to change the approach to regulation. Unlike the previous directive, the IDD applies to a much wider group of people. In addition, current regulations indicate strong modeling on the MiFID II (Directive EU 2014/65/EU). The main purpose of the IDD is to ensure an equal level of protection for clients regardless of the insurance distribution channel. Particularly important is the provision which obliges the insurance distributor to specify, on the basis of information received from the client, their requirements and needs and to provide that client in an intelligible form with objective information about the insurance product. This is to enable clients to make an informed and rational decision. Finally, mis-selling issues were regulated. If the insurance product is offered with another product, then the distributor should inform whether it is possible to purchase these products

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separately. If so, he is required to provide a description and a separate statement of costs and fees for each component of the contract. In addition to the general regulations, the directive also includes additional requirements related to the provision of insurance-based investment products in the field of preventing conflicts of interest, information provided to clients, including costs and fees, as well as assessing suitability and appropriateness and reporting to clients. The breakthrough solution from the point of view of repairing the situation of clients who have felt threatened or deceived so far due to the lack of knowledge of fees and costs or an inappropriate investment strategy is the provisions of Art. 29, paragraph 1 (Directive EU 2016/97). From now on, the broker or insurance company should provide the client with information on a periodic assessment of the suitability of products and strategies recommended to them, tips and warnings regarding the risk of insurance-based investment products, and information on product distribution, including consultancy costs, the insurance cost of an insurance-based investment product, and how fees are paid by the client, which is to force insurance intermediaries to disclose incentives and remuneration received from insurance companies. In addition, information on all costs and fees (including distribution costs and fees) that do not arise from the underlying market risk is presented collectively to enable the customer to understand the total cost of the product and the cumulative impact on return on investment. According to the European Commission, the provisions of the IDD have been fully implemented in 27 member countries, which is 96% of all countries that should do so. The provisions have been partially implemented only in Spain. In addition, the Commission is currently carrying out enforcement actions against 14 member states for violating the provisions of the directive. The infringement procedures initiated by the Commission are related to the absence or delay of notification of national transposition measures and their incompetence. Such proceedings are currently pending against countries such as Austria, Belgium, Bulgaria, Croatia, Cyprus, France, Germany, Greece, Latvia, Luxembourg, Portugal, Romania, Slovenia, and Spain (European Commission 2019).

6.5

Assumptions for the New Insurance-Based Investment Product Distribution Model

Since the 2002 reform, Europe has relied on effective competition to achieve sales discipline in insurance, which is seen with success. Consumer protection itself is seen by the European authorities as providing potential customers with comprehensive product information (Pradier and Chneiweiss 2016). New EU legal regulations that had to be implemented in individual member countries force distributors to ensure an adequate standard of consumer protection before selling a product. In the future, this should result in avoiding mis-selling and

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Table 6.2 A detailed scope of distributors’ activities in product approval processes according to the new sales model Stage 1

Stage 2

Stage 3 Stage 4

Determination of the target market for each product together with an assessment of all significant threats Identification of market segments for which the insurance product is not suitable An analysis of the insurance product in terms of its effectiveness in various scenarios with a crisis burden Conducting an analysis of the insurance product for solutions unfavorable to the client and making possible modifications to mitigate the damage Identification of appropriate distribution channels, taking into account the target product market Verifying that the activities undertaken in the distribution channels are consistent with the supervisory and product management arrangements adopted by the insurance undertaking

Source: author’s own research based on Final Report. . . (2016) Table 6.3 Necessary areas of change under the new insurance distribution model Areas in dealing with clients Customer needs analysis Marketing materials Information obligations Complaints

Areas in the activities of distributors Remuneration and commissions Product management Authorization for distribution activities Conflict of interest management

Source: author’s own research

the situation of offering the client insurance that does not match their needs and may also affect rebuilding positive image and increased confidence in the insurancebased investment product industry. The new regulations should shape a new life insurance sales model, whose main goals must be based on the same level of protection, sales standards, and customer information, regardless of the sales channel used through: 1. Improving the processes of providing accurate information to clients about: (a) Product possibilities related to the cost-saving part (b) Additional costs and level of investment risk 2. Assuming minimum requirements for completeness, transparency, and adequacy of information on specific insurance products 3. Effective regulation of product sales processes An approval of products that are in the market and will be offered by distributors should be preceded by specific actions of distributors (Table 6.2). Major changes should take place at specific stages at the level of customer contact and specific activities of all distributors. Specific business processes in which changes should be introduced should cover both areas of contacts with customers and the activities of distributors (Table 6.3).

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In the new insurance-based investment product distribution model, taking into account the market experience to date in the fight against unfair behavior of distributors, the issue of compliance with the important obligations of the distributor, and especially the behavior of his activities, which should be carried out in the following order, becomes extremely important: 1. Obtaining information from the client clearly stating their needs 2. Checking if the potential customer is in the group of potential buyers of the target market 3. Offering a product tailored to the needs and in line with the needs of the customer 4. Explanation and discussion of product design to facilitate making an informed decision related to the purchase or resignation of a product 5. Informing the customer about the amount of costs and remuneration of the distributor

6.6

Conclusions

Initiating the discussions on the problems of unfair market practices by insurance distributors has proved to be an effective tool that has resulted in a number of actions, including stricter regulations regarding increased market transparency from the point of view of its customers and increased interventions regarding compliance with good practice principles. All regulations that improve the situation and consumer safety from the moment one receives some advice on purchasing insurance-based investment products are of great importance, and their essence should be a new element of protection policy in the form of a new insurance sales model. It depends only on the honest approach of distributors and their compliance with the rules established in the implementation of the new insurance sales model whether trust in investment insurance institutions will be restored in the future. Undoubtedly, setting new sales standards, increasing the sense of security of customers who have products fully customized to their needs, and, above all, increasing the regime in the field of information transparency are now an opportunity for the development of the market of insurance-based investment products, rebuilding mutual trust of market participants and fixing existing errors, especially as part of financial advisory services operating rather badly. The effectiveness of the changes introduced by IDD can be demonstrated by a high percentage of countries that have already implemented provisions in their legal systems and a relatively small number of revealed infringements of the provisions of the Directive.

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References Baker, A. (2013). The new political economy of the macroprudential ideational shift. New Political Economy, 18(1), 112–139. Christofilou, A. (2014). Cross selling practices in insurance products and the IMD2 proposal. European Insurance Law Review, 4, 66–68. Committee on the Global Financial System. (2008). Ratings in structured finance: what went wrong and what can be done to address shortcomings? Report submitted by a Study Group established by the CGFS Papers (Bank for International Settlements No 32, pp. 1–29). Czechowska, I. D., & Waliszewski, K. (2018). Mis-selling in finance as an effect of excessive concentration on sales. Przedsiębiorczość i Zarządzanie, 19(1), 20–24. Directive EU 2002/92/EC of the European Parliament and of the Council of 9 December 2002 on insurance mediation, OJ L 009 of 15/01/2003. Directive EU 2009/138/EC of the European Parliament and of the Council of 25 November 2009 on the taking-up and pursuit of the business of Insurance and Reinsurance (Solvency II), OJ L 335 of 17/12/2009. Directive EU 2014/65/EU of the European Parliament and of the Council of 15 May 2014 on markets in financial instruments and amending Directive 2002/92/EC and Directive 2011/61/ EU, OJ L 173 of 12/6/2014. Directive EU 2016/97 of the European Parliament and of the Council of 20 January 2016 on insurance distribution, OJ L 26 of 2/2/2016. Edelman Trust Barometer. (2019). Financial services, Edelman Annual Global Study, pp. 2–11. European Insurance Overview. (2018). Publications Office of the European Union, Luxembourg (pp. 7–8), https://doi.org/10.2854/168899 Financial Stability Report. (2019). EIOPA: Publications Office of the European Union, Luxembourg (pp. 19). https://doi.org/10.2854/71621 Fisher, P. (2008). What happened to risk dispersion? Financial Stability Review, 11, 27–38. Insurance distribution directive–transposition status. European Commission. Accessed November 17, 2019., from https://ec.europa.eu/info/publications/insurance-distribution-directive-transposi tion-status_en Kawiński, M. (2015). Ochrona konsumenta w agendzie Europejskiego Nadzoru Ubezpieczeń i Funduszy Emerytalnych. Kierunki zmian. In J. Monkiewicz & M. Orlicki (Eds.), Ochrona konsumentów na rynku ubezpieczeniowym w Polsce (p. 187). Warszawa: Poltext. Kiesenbauer, D. (2011). Main determinants of lapse in the German life insurance industry. North American Actuarial Journal, 16(1), 52–73. Kościelniak, M. (2016). Ubezpieczenia na życie z ubezpieczeniowym funduszem kapitałowym – doświadczenia wybranych państw europejskich. Rozprawy Ubezpieczeniowe, 1, 36. Łańcucki, J. (2015). Ochrona konsumentów w sektorze ubezpieczeń w regulacjach unijnych. Determinanty skuteczności przyjętych rozwiązań. Prawo Asekuracyjne, 4(85), 14–15. Monkiewicz, J., & Monkiewicz, M. (2015). Tendencje rozwoju ochrony konsumentów na rynku ubezpieczeniowym. Nowe koncepcje i rozwiązania, Rozprawy Ubezpieczeniowe, No 18/(1/2015), p. 11. Muller, P., Devnani, S., Heys, R., & Suter, J. (2014). Consumer protection aspects of financial services (p. 24). Brussels: Directorate-General for Internal Policies, European Parliament. Poufinas, T., & Michaelide, G. (2018). Determinants of life insurance policy surrenders. Modern Economy, 9(8), 1400–1418. Pradier, P. C., & Chneiweiss, A. (2016). The evolution of insurance regulation in the EU since 2005. Documents de Travail du Centre d’Economie de la Sorbonne, Université Paris1, Panthéon-Sorbonne (CES Working Papers, p. 6) Regulation of the European Parliament and of the Council of the European Union 2014/1286 of 26 November 2014 on documents containing key information on retail investment products (PRIIP), OJ L 352 of 9/12/2014.

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Regulation of the European Parliament and of the Council 2016/1904 of 14 July 2016 on supplementing Regulation 2014/1286 with regard to product intervention, OJ L 296 of 29/10/ 2016. Seventh Consumer Trends Report. (2018). EIOPA: Publications Office of the European Union, Luxembourg, p. 11–12; 31–32. https://doi.org/10.2854/99323 Surrenders in the Life Insurance Industry and their Impact on Liquidity. (2012). Geneva/Basel: The Geneva Association (The International Association for the Study of Insurance Economics), pp. 12–13 The Price of Bad Advice. (2018). BEUC Position Paper. The European Consumer Organisation, pp. 3–4. Wiśniewski, M. (2016). Ubezpieczenia o charakterze oszczędnościowym w świetle nowych regulacji. Wiadomości Ubezpieczeniowe, 3, 72.

Chapter 7

Analysis of Capital Requirements in Life Insurance Sector Under Solvency II Regime: Evidence from Poland Dorota Jaśkiewicz

7.1

Introduction

After several years of preparation, on January 1, 2016, the Solvency II system came into force. According to the regulations, the capital requirement is equal to the higher of solvency capital requirement (SCR) and minimum capital requirement (MCR). The SCR has replaced the solvency margin and is the target risk measure for insurance and reinsurance undertakings. In the Solvency II system, insurance companies can determine the SCR using the standard formula or an internal model. Despite substantial preparation, the SCR is a new solution and thus doubts continue to emerge over its implementation. The financial security of the insurance sector is an important issue for both the financial market and its individual clients. Situations have arisen in the past where insurance companies have become insolvent. This means that, from the client’s point of view, the financial stability of the insurance company should be verified before entering into an insurance contract. For instance, the German insurance company Mannheimer declared its business to have failed in 2003. The bankruptcy was caused by risky speculations in shares. The German Insurance Association and Germany’s Federal Financial Supervisory Authority (BaFin) then sought to limit the damage done to the life insurance market. To secure the position of their clients, more than 300,000 policies with an estimated EUR 3 billion in assets were transferred to a newly founded entity. Radice (2010) states that, following this, “All German life insurers had then undertaken to contribute, if necessary, a total of up to 1% of their net provisions to ensure that, in an emergency, policyholders of struggling insurers receive at least the guaranteed benefits to which they are entitled.”

D. Jaśkiewicz (*) Wroclaw University of Economics and Business, Wrocław, Poland e-mail: [email protected] © Springer Nature Switzerland AG 2020 M. Borda et al. (eds.), Life Insurance in Europe, Financial and Monetary Policy Studies 50, https://doi.org/10.1007/978-3-030-49655-5_7

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The implementation of Solvency II on the insurance market has had several positive outcomes. For instance, in May 2018, Insurance Europe carried out a survey of 87 insurers from 17 European Union markets. The results showed that over three quarters of the respondents had observed a positive effect of the EU’s 2016 Solvency II regulation on their risk management and governance practices as well as their management of assets and liabilities (Insurance Europe 2018). Furthermore, Karel Van Hulle—head of the European Commission’s insurance and pensions unit during the development of Solvency II—claimed that the new regime has established a link between risk and capital (Van Hulle 2019). According to Karel Van Hulle (2019), “this leads to a more professional way of conducting insurance business. From the conception of the insurance product, through the sales process and the claims handling, insurers will have to be mindful about the capital consequences of the risks that they are taking.” Solvency II provides a wealth of experience that other countries who want to improve their solvency regime can draw upon. According to Karel Van Hulle, “the Solvency II has had a great impact on regulatory developments in other parts of the world. Many countries are in the process of reviewing their solvency regime” (ERM 2019). To focus on the problem of solvency among insurance companies, it is first important to consider the evolution of the concept of solvency and approaches toward it. In 1984–1985, the Faculty of Actuaries defined solvency as “meeting obligations that arose or may arise in the future, taking into account possible assumptions and a certain degree of probabilit”’ (Hardie 1984–1986). In 1986, Kastelijn emphasized the importance of commitments, arguing that solvency means “having sufficient financial means to cover all financial commitments.” In the Muller report of 1997, solvency was defined as “the appropriate amount of financial resources of insurance companies, i.e. in fact the difference between the assets held and the liabilities of the insurance company” (UNION 1997). Similarly, in 2000, the International Association of Insurance Supervisors (IAIS) described solvency as “the ability of insurance companies to fulfil their obligations under all contracts at all times”(IAIS 2000). Regarding the solvency of an insurance company, it is essential to consider clients’ point of view, from those who already own the product of a given insurance company to those who are simply looking for insurance protection. Whatever the status of the client, the key to maintaining the solvency of an insurance company is to ensure all obligations are covered. In essence, the insurance contract is an agreement by which the insurance undertaking obligates, in the event of an insured event, to pay a certain amount of money, compensation, annuity, or other benefit, while the policyholder undertakes to pay the insurance premium. There are three components to the insurance financial system: the reality, completeness, and universality of insurance protection. The most important of these is the reality of insurance coverage, which reflects a need to provide insurance certainty. Bijak (2003) defined solvency as “the value of assets free from any liabilities towards the insured, defined as a safety buffer”. From 2009, this concept developed into: “the state of dynamic balance between the assets of the insurance company and liabilities resulting from

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insurance contracts and cash flows generated by assets and liabilities” (Bijak 2009). When an insurance event occurs, the insurer will meet their obligation to pay the benefit within the statutory period and at the appropriate amount. The principle of the reality of insurance protection entails the need for both a legal guarantee and an economic guarantee. The legal guarantee constitutes the right to enforce and pursue claims against the insurance undertaking. Conversely, the economic guarantee stipulates that, with respect to the payment of benefits, the insurance company will have adequate financial resources in place to ensure the liquidity and solvency of the company. This issue is strongly reflected in the definition provided by IAIS (2000) according to which “solvency is the ability of an insurance undertaking to meet its obligations under all concluded contracts at any time.” A solvent entity can thus meet any obligations that have arisen or may arise in the future. Lisowski (2010) emphasizes that, from the client’s perspective, there are two dimensions: “firstly, the ability to meet obligations, and therefore the financial situation,” and “secondly, the willingness, and therefore the will to fulfil them.” Lisowksi further indicates that “legal regulations give the insured the possibility of pursuing claims against an insurance company through legal means—legal guarantees of the reality of insurance protection. However, the elements of the financial strength of the insurance company come to the fore—economic guarantees of the reality of insurance protection.” The purpose of this chapter is to present the results of the first years of the Solvency II system with respect to capital requirements. The focus is on the public disclosure of solvency positions and the risk profiles of life insurance companies operating in Poland. The specific focus is on the coverage of SCR, MCR, and components of SCR for all active life insurers in Poland.

7.2

Analysis of SCR Ratio

From first January 2016, the SCR was calculated according to the standard formula, which is formulated as follows: SCR ¼ BSCR þ SCRop þ Adjustment

ð7:1Þ

where SCR denotes solvency capital requirement, BSCR denotes basic solvency capital requirement, SCRop denotes the capital requirement deriving from operational risk, and adjustment denotes an adjustment for the ability of reserves and deferred taxes to cover losses. The BSCR includes the following risks: market risk module, counterparty default risk module, life underwriting risk module, nonlife underwriting risk module, health underwriting risk module, and intangible asset risk module and is calculated using the following formula:

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sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi X  BSCR ¼ Matrixi,j ∙ SCRi ∙ SCR j þ SCRintagibles

ð7:2Þ

i, j

where SCRi and SCRj are the relevant modules of the BSCR, SCRintagibles denote the capital requirement for the risk of intangible assets, and Matrixi, j is a dependency matrix defined in Annex 4 of the Solvency II directive (Solvency 2009). The SCR is presented in the solvency and financial condition report (SFCR) of each insurance company under section E2, entitled: Solvency Capital Requirement and the Minimum Capital Requirement. All insurance undertakings are obliged to publicly disclose their solvency and financial condition report (SFCR) annually on the company’s website. For the purpose of this article, the author analyzed the entire SFCR with respect to the scope of capital requirements. In the first part of the analysis, an international comparison of SCR ratio and MCR ratios was calculated for European countries in 2017, as presented in Table 7.1. The weighted average in Table 7.1 represents the aggregate own funds (sum for all insurers) divided by aggregate SCR or MRC, respectively. The figures show that the entire European Insurance industry is well capitalized. According to data published by EIOPA, the average value of SCR ratio for all European countries in 2017 was 239%, and the average MCR ratio was equal to 648% (Statistics 2018). In 2016 and 2017, no Polish insurance company had obtained consent from the Polish Financial Supervision Authority to calculate the SCR using a full or partial internal model. Therefore, all insurance companies were using the standard formula. Figure 7.1 presents the results of the analysis in respect of SCR coverage by eligible own funds, the so-called SCR Ratio. In both 2016 and 2017, all the insurance undertakings were solvent according to the Solvency II directive, which meant that all the companies in a given period of time had a SCR ratio higher than 100%. The top five insurers in 2016 with the highest SCR ratio were METLIFE TUnŻiR S.A. (544%), MACIF ŻYCIE TUW (459%), TU na Życie EUROPA S.A. (426%), GENERALI ŻYCIE T.U. SA (410%), and PZU ŻYCIE SA (396%). In 2017, the top five companies remained the same with the following results: METLIFE TUnŻiR S.A. (537%), GENERALI ŻYCIE T.U. SA (477%), MACIF ŻYCIE TUW (477%), PZU ŻYCIE SA (437%), and TU na Życie EUROPA S.A. (349%). As shown in Fig. 7.1, the average value of the SCR ratio was 279% and 287% in 2016 and 2017, respectively. The companies with the lowest coverage of SCR in 2016 were OPEN LIFE TU ŻYCIE S.A., VIENNA LIFE TU na ŻYCIE S.A. Vienna Insurance Group, PKO ŻYCIE TU S.A., POCZTOWE TUnŻ S.A., and PRAMERICA ŻYCIE TUiR SA. In 2017 the companies with the lowest coverage of SCR were VIENNA LIFE TU na ŻYCIE S.A., OPEN LIFE TU ŻYCIE S.A., POCZTOWE TUnŻ S.A., AEGON TU na ŻYCIE S.A., and PRAMERICA ŻYCIE TUiR SA.

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Table 7.1 Solvency ratios for European countries in 2017 Country Austria Belgium Bulgaria Croatia Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Hungary Iceland Ireland Italy Latvia Liechtenstein Lithuania Luxemburg Malta Netherlands Norway Poland Portugal Romania Slovakia Slovenia Spain Sweden United Kingdom Total

Weighted average of SCR ratio (%) 283 192 217 241 255 229 289 192 206 238 345 173 225 150 176 241 134 213 186 216 329 183 210 256 175 178 196 238 236 254 155 239

Weighted average of MCR ratio (%) 896 421 480 664 689 657 743 555 702 594 958 452 587 366 489 608 282 612 461 628 791 433 525 734 533 412 518 739 608 904 453 648

Source: EIOPA [Solo/Annual/Published 20181113/Data extraction completed 20181108]

7.3

Analysis of MCR Ratio

In accordance with Articles 128–129 of the Solvency II directive (Solvency 2009), the MCR should guarantee a minimum level of capital below which insurer’s funds should not decrease. The MCR is calculated as follows: MCR ¼ max (MCRcombined; AMCR), where MCRcombined is the MCR, and AMCR is the absolute limit for MCR. The value of the absolute limit for the MCR (AMCR) threshold is established as:

SCR Ratio 2016

SCR Ratio 2017

Average 2016

SCR Ratio 2016-2017

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Fig. 7.1 Solvency capital requirement in 2016 and 2017 for life insurers in Poland. Source: Own analysis based on Solvency and Financial Condition Report

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(a) EUR 2200000 for nonlife insurance companies, including related insurance companies, except for those taking risks from groups 10–15 (i.e., liability insurance for the use of motor vehicles, third-party liability insurance for aircraft, liability insurance for the use of vessels operating in sea and inland navigation, general liability insurance, credit insurance, insurance of sureties), in which case the threshold is EUR 3,200,000 (b) EUR 3,200,000 for life insurance companies, including related insurance companies (c) EUR 3,200,000 for reinsurance companies, with the exception of captive reinsurers for which the MCR is at least EUR 1,000,000 The sum of the amounts listed in (a) and (b) are used for insurance companies that simultaneously conduct life insurance and nonlife insurance activities However, in Poland, according to the new Insurance Act (Act, 2015), the requirements for Polish insurance undertakings are even more severe. For instance, the requirements for absolute minimum capital requirements are as follows: (a) 2,500,000 EUR for nonlife insurance companies, which carry out business in 1–9 and 16–18 Polish KNF’s insurance groups, (b) 3,700,000 EUR for nonlife companies, which carry out business activity in 10–15 Polish KNF’s insurance groups, (c) 3,700,000 EUR for life companies, and (d) 3,600,000 EUR for reinsurance companies. As mentioned previously, information regarding capital requirements is presented by insurers in SFCRs in Part E2 of the document. This includes the MCR. Figure 7.2 presents the results of MCR Ratio, the coverage of MCR by eligible own funds, for all Polish insurance undertakings in 2016 and 2017. The top five insurers in 2016 with the highest SCR ratio were TU na Życie EUROPA S.A. (1643%), PZU Życie SA (1510%), METLIFE TUnŻiR S.A. (1210%), NATIONALE NEDERLANDEN TUnŻ S.A. (1070%), and AVIVA TUnŻ S.A. (1043%). In 2017, the top five companies were TU na Życie EUROPA S.A. (1344%), METLIFE TUnŻiR S.A. (1193%), AXA ŻYCIE TU S.A. (1093%), GENERALI ŻYCIE T.U. SA (1059%), and NATIONALE NEDERLANDEN TUnŻ S.A. (1052%). As shown in Fig. 7.1, the average value of the SCR ratio was 690% and 692% in 2016 and 2017, respectively. The companies with the lowest coverage of SCR in 2016 were MACIF ŻYCIE TUW, POCZTOWE TUnŻ S.A., SIGNAL IDUNA ŻYCIE POLSKA TU S. A., TU SKOK ŻYCIE SA, and the POLISA-ŻYCIE TU S.A. Vienna Insurance Group. In 2017, the companies with the lowest coverage of SCR were MACIF ŻYCIE TUW, POCZTOWE TUnŻ S.A., TU INTER-ŻYCIE POLSKA S.A., SIGNAL IDUNA ŻYCIE POLSKA TU S.A., and SALTUS TU ŻYCIE SA.

MCR Ratio 2016

MCR Ratio 2017

Average 2016

MCR ratio 2016-2017

Average 2017

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Fig. 7.2 Minimum capital requirement in 2016 and 2017 for life insurers in Poland. Source: Own analysis based on Solvency and Financial Condition Report

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Analysis of Solvency Capital Requirement Components

The BSCR consists of appropriate sub-modules, which are presented in Fig. 7.3. The standard formula is modular, which means that in the first stage, the insurance company identifies individual types of risk based on its own risk profile; it then sets the capital requirement for each type of risk and aggregates the individual capital requirements using a dependency matrix. The BSCR modules have been calibrated to take account of the value at risk (VaR) measure of the insurer’s own funds at a confidence level of 0.995 over a 1-year period. The composition of the SCR and share of particular risk modules in the SCR are presented in Figs. 7.4 and 7.5 for 2016 and 2017, respectively. The analysis shows that in both 2016 and 2017, the key risk among the risk profiles of insurance companies was the life underwriting risk (59% and 82% of SCR, respectively), which is typical for the life insurance sector. The second most significant risk module significance was market risk, which determined almost 57% of the SCR in 2016 and 44% of SCR in 2017. The health underwriting risk accounted for 6% and 13% of the SCR in 2016 and 2017, respectively. The counterparty risk during the period was 4% and 6% in 2016 and 2017, respectively. The greatest impact of this life underwriting risk module on the SCR was recorded for the following insurance companies: AXA ŻYCIE TU S.A., AEGON TU na ŻYCIE S.A., and GENERALI ŻYCIE T.U. SA in 2016, and PZU ŻYCIE SA and AVIVA TUnŻ S.A. in 2017. Market risk had a significant impact on the amount of the SCR. In 2016, the highest value for the market risk component was observed for AXA ŻYCIE TU S.A., and in 2017, it was observed for PZU ŻYCIE SA. The BSCR calculated on the basis of risk modules can be reduced using the diversification effect. This is a consequence of the SCR estimation method and consists of the aggregation of many types of insurer risk, although not all are realized at the same time. Consequently, the total value of capital necessary to hedge against these risks is generally smaller (or equal) to the sum of the capital required to hedge each of them separately. AXA ŻYCIE TU S.A. in 2016 and PZU ŻYCIE SA in 2017

Solvency Capital Requirement (SCR)

Adjustment

Market Risk

Health Underwriting Risk

Basic Solvency Capital Requirement (BSCR)

Default Counterparty Risk

Life Underwriting Risk

Operational Risk

Non-life Underwriting Risk

Intangibles Risk

Fig. 7.3 Composition of solvency capital requirement and basic solvency capital requirement. Source: The underlying assumptions in the standard formula for the Solvency Capital Requirement calculation, EIOPA

Diversification

Fig. 7.4 Components of SCR in 2016 for Polish life insurers

Counterparty risk Operational risk

Market risk

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Fig. 7.5 Components of SCR in 2017 for Polish life insurers

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benefited the most from diversification effects as they managed to reduce their capital requirements by more than 30%.

7.5

Conclusions

The aim of the research was to provide a clear picture of the solvency position of all life insurance companies operating in Poland in 2016–2017. The literature review and ratio analysis were based on quantitative data provided by the insurance companies in QRT templates and information provided in SFCR reports. The main findings can be summarized as follows: • All examined insurance companies set the SCR using the standard formula. • All the insurance companies had SCR coverage using eligible own funds in both 2016 and 2017. • In 2016 and 2017, all life insurers had MCR coverage using eligible own funds. • For the life insurance sector in 2016, life underwriting risk had the greatest impact on the overall capital adequacy requirement, representing 59% of SCR. The next greatest was market risk, constituting 57% of SCR. • For the life insurance sector in 2017, life underwriting risk had the greatest impact on the overall capital adequacy requirement, representing 82% of SCR. The next greatest was market risk, constituting 44% of SCR. The analysis therefore showed that all life insurance companies in Poland were able to fulfill the new capital requirements resulting from the implementation of the Solvency II system and possessed the requisite coverage of capital requirements using their own funds. It also confirmed that in the life insurance business, the life underwriting risk, and the market risk were the most important components influencing the level of solvency of an insurer. The revision of the standard formula for calculating the SCR is the new challenge (re)insurance undertakings now have to face. The changes, updates, and reconciliations applied to standard formula parameters will be a new task for entities in upcoming years. They may also be the starting point in terms of the implementation of an internal model on the Polish market.

References Bijak, W. (2003). Zewnętrza ocena zakładu ubezpieczeń. In J. Monkiewicz (Ed.), Podstawy ubezpieczeń. Przedsiębiorstwo. Warszawa: Poltext. Bijak, W. (2009). Praktyczne metody Badania niewypłacalności zakładów UBEZPIECZEŃ (1st ed.). Warszawa: Oficyna Wydawnicza SGH. ERM. (2019). Solvency II’s biggest unintended consequences. https://www.insuranceerm.com/ analysis/solvency-iis-biggest-unintended-consequences.html

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Insurance Europe. (2018). Survey shows Solvency II brings benefits but deters long-term business. https://www.insuranceeurope.eu/survey-shows-solvency-ii-brings-benefits-deters-long-termbusiness Hardie, A. (1984–1986). The solvency of life assurance companies. Transactions of the Faculty of Actuaries, 38, 251–340. IAIS. (2000) On solvency, solvency assessments and actuarial issues (An IAIS Issues Paper). Lisowski, J. (2010). Specyfika gospodarki finansowej ubezpieczycieli kredytu kupieckiego W POLSCE, Poznań. Redice, M. (2010). Assessing the potential for systemic risks in the insurance sector, Considerations on insurance in Switzerland (FINMA Working Paper June/2010). Solvency. (2009). Directive 2009/138/EC of the European Parliament and of the Council of 25 November 2009 on the taking-up and pursuit of the business of Insurance and Reinsurance (Solvency II). Statistics. (2018). EIOPA statistics. EIOPA https://eiopa.europa.eu/Publications/Insurance%20Sta tistics/SA_Accompanying_note.pdf UNION. (1997). Report solvency of insurance undertakings, Berlin. Van Hulle, K. (2019). Solvency requirements for EU insurers: Solvency II is good for you (1st ed.). Frankfurt: Intersentia.

Part II

Innovations and Risk Analysis

Chapter 8

Longevity-Linked Annuities: How to Preserve Value Creation Against Longevity Risk Annamaria Olivieri and Ermanno Pitacco

8.1

Introduction

Among the private post-retirement income solutions, traditional life annuities offer significant longevity and financial guarantees. Indeed, a minimum annual amount is paid lifelong, whatever the return on investment, the lifetime of the individual and the mortality of the pool. However, the longevity risk taken in this way by providers endangers the creation of business value, as they are dealing with a long-term risk that is difficult to predict. Longevity risk management has been largely discussed in the literature of the last 30 years. In particular risk transfer solutions, preferably to the capital market, have been extensively investigated. However, it is still difficult to find a counterparty for the transfer of longevity risk, due to its systematic and long-term nature; see, for example, Blake et al. (2019) for an overview of the market development. On the other hand, increasing the annuity loading to get a reward for the accepted longevity risk is not an option for annuity providers, given that individuals already consider annuities to be expensive. In recent times, the design of longevity guarantees has been reconsidered. Some authors have explored the idea of sharing the risk between annuitants and the provider, through participating structures, providing a link to some longevity experience. In particular, the benefit amount should be allowed to decrease, possibly maintaining a guaranteed minimum amount, in case of a longevity loss. This should be balanced by a reduction of the annuity loading or a participation to longevity profit.

A. Olivieri (*) University of Parma, Parma, Italy e-mail: [email protected] E. Pitacco University of Trieste, Trieste, Italy © Springer Nature Switzerland AG 2020 M. Borda et al. (eds.), Life Insurance in Europe, Financial and Monetary Policy Studies 50, https://doi.org/10.1007/978-3-030-49655-5_8

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Different terms have been used in the literature to define annuity designs in which the benefit amount is linked to the mortality/longevity experience: for example, Lüthy et al. (2001) define adaptive algorithmic annuities, Denuit et al. (2011) define longevity-indexed life annuities, Denuit et al. (2015) define longevity-contingent life annuities, Richter and Weber (2011) define mortality-indexed annuities and Bravo and de Freitas (2018) define longevity-linked life annuities. Further, several examples of adjustment coefficients of the benefit amount can be found. Lüthy et al. (2001) address the ratio between the actuarial value of the annuity obtained with different mortality forecasts, namely, the initial and the latest. Denuit et al. (2015) examine a similar ratio, but without discounting (indeed, they consider the expected lifetime, which is equivalent to the actuarial value of the annuity using a 0% discount rate). Denuit et al. (2011) and Bravo and de Freitas (2018) work with the ratio between survival probabilities, comparing the expected to the realized proportion of survivors in a reference population. Richter and Weber (2011) compare the available reserve to the updated actuarial value of the annuity. Maurer et al. (2013) also consider the ratio between reserves when discussing variable investment-linked deferred annuities (VILDAs). These alternative linking solutions can be obtained as particular cases of a general structure, as described by Olivieri and Pitacco (2020). The idea of longevity-linked benefits is not new. Forms of participation to the longevity experience were already present in tontine annuities. These are arrangements which date back to the seventeenth century and were designed as financial annuities, with the annual or final amount increased using the funds released by the deceased. Tontine annuities were originally developed for speculative purposes; in recent times, they have been revised as a possible solution for coping with longevity risk. See, for example, McKeever (2009), Baker and Siegelman (2010), Sabin (2010), Milevsky (2014), Milevsky and Salisbury (2015), Milevsky and Salisbury (2016), Weinert and Gründl (2016), Chen et al. (2019) and Chen and Rach (2019). Outside the insurance framework, examples of annuity benefits implying a longevity linking are provided by the well-known group self-annuitization schemes (see, e.g., Piggott et al. 2005; Valdez et al. 2006; Qiao and Sherris 2012), as well as by pooled annuity funds (see Stamos 2008; Donnelly et al. 2013) and annuity overlay funds (see Donnelly et al. 2014; Donnelly 2015). These are self-insured arrangements. They are based on the principle that liabilities must always be funded; such a target is realized by letting the benefit amount decrease, if the available asset amount is lower than the required reserve. Self-insured arrangements rely on pooling arguments; they do not provide explicit longevity guarantees, and they are unable to absorb systematic losses originated by unanticipated mortality improvements. Conversely, thanks to the pool size they should be able to offset random fluctuations, without having to charge fees (as it happens in insured arrangements), as no guarantee is provided. In this paper we focus on insurance arrangements, in which guarantees are provided and a fee is charged, and we explore the emerging business value. Linking the annuity benefit amount to the longevity experience determines two opposing effects on such a value: possible losses are reduced but also possible profits. The trade-off is not obvious and requires appropriate assessment metrics. In particular,

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we follow a market consistent embedded value (MCEV) logic, and we assess the business value as the difference between the present value of future profits and the cost of capital, which is expressed in terms of frictional costs. See, for example, Blackburn et al. (2017) for a review and an application of a MCEV assessment to a traditional annuity business, allowing for longevity risk. In respect of the present value of future profits, we perform an investigation of annual profits, identifying their main components within a longevity-linked arrangement, in order to better understand the impact of alternative choices of the linking coefficient. As for capital, we keep in line with the Solvency II regulation; however, the standard formula for the required capital may not capture appropriately the longevity risk to which the provider is exposed. Thus, we avoid using such a formula, while we define the capital size considering explicitly the riskiness retained by the provider; this way, the business value can better express the risk-return trade-off for the provider. This paper further develops Olivieri and Pitacco (2020), in particular with regard to the value to the provider of a longevity-linked annuity. While Olivieri and Pitacco (2020) already consider the present value of future profits at a generic time t, we aim to go deeper into the analysis of how such a value does emerge in time; further, as already mentioned, we also include capital. Similarly to Olivieri and Pitacco (2020), we consider alternative designs of the longevity guarantee, and we focus on aggregate longevity risk only. The chapter is organized as follows. In Sect. 8.2 we describe a general structure for longevity-linked post-retirement benefits and provide some particular cases of interest in insurance applications. The business value is examined in Sect. 8.3. A numerical experiment is developed in Sect. 8.4. Finally, in Sect. 8.5 we conclude with some final comments.

8.2 8.2.1

Longevity-Linked Annuity Benefits: From a General Expression to Particular Solutions A General Expression

In this section, we summarize the general structure for longevity-linked annuity benefits described in Olivieri and Pitacco (2020), to which we refer for further details. We consider a discrete-time annuity immediate in arrears, i.e. with payments at the end of the year. For simplicity, one cohort only is addressed, aged x at entry time 0. We denote with S the initial amount paid by each policyholder. In this paper, we focus on longevity risk, while the financial setting is deterministic. For simplicity, we then assume a fixed interest rate, at a level i. Conversely, we adopt a stochastic mortality model (which is described in Sect. 8.4.1). In this section, we only provide the notation, as well as the meaning and role, of the various mortality measures. The best-estimate assumption at time h about the death

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probability at age x + t is denoted as qx + t(h), while px + t(h) denotes the best-estimate annual survival probability (at the same age and time). The actuarial value at time t, age x + t, of a unitary discrete-time annuity in arrears, based on the best-estimate assumptions F h at time h, 0
h
t, is denoted as ax + t(h) and is computed as follows: axþt ðhÞ ¼ ½aK xþt j F h  ¼

ωðxþtÞ X

ð1 þ iÞs  s pxþt ðhÞ,

ð8:1Þ

s¼1

where Kx + t is the curtate lifetime at age x + t and ω is the maximum attainable age (which is assumed to be known). Since it is based on best-estimate assumptions, we mean that no loading is included in ax + t(h). We assume that the initial benefit amount, b0, is assessed as follows: b0 ¼ S 

1 , ax ð0Þ  ð1 þ πÞ

ð8:2Þ

where π represents the premium loading, whose size is defined at time 0, depending on the risk retained by the provider, as we discuss in detail in Sect. 8.3.1. If the annuity is fixed amount, the benefit amount bt at time t, t ¼ 1, 2, . . ., is simply bt ¼ b0. If the annuity is longevity-linked, we admit that the benefit amount at time t can be revised, according to a chosen longevity experience. Depending on the annuity design, the longevity experience can be measured in a portfolio, in a reference population or with a (projected) life table, and can be expressed in terms of proportion of survivors (or, equivalently, survival probabilities) or in terms of actuarial quantities (for example, the actuarial value of the annuity). Some clearer ideas about such alternatives emerge in the discussion that follows. Assume that the benefit adjustment is applied annually. For a policy in-force at time t  1, we refer to the following actuarial balance for year (t  1, t): bt1  axþt1 ðτ0 Þ  ð1 þ iÞ ¼ bt  ð1 þ axþt ðτ00 ÞÞ  ~pxþt1 ,

ð8:3Þ

which resembles the well-known recursion of the mathematical reserve (see, for example, Olivieri and Pitacco 2015). We point out that in Eq. (8.3), parameters are meant to be defined according to policy conditions and are subject to update. In particular: • τ0 , 0
τ0
t  1, and τ00 , τ0
τ00
t, are the times when the life table for the assessment at times t  1 and t of the actuarial value of the annuity are respectively set. • ~ pxþt1 represents the survival probability measuring the mortality credit assigned to the policy account value for year (t  1, t). The probability ~pxþt1 is set depending on the mortality observed in a chosen population, with possible guarantees. Some examples are discussed in Sect. 8.2.2.

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We point out that whenever ~pxþt1 and ax + t(τ00 ) are not fixed at the beginning of the year (i.e. time t), but depend (respectively) on the observed mortality and the possible update of the life table during year (t  1, t), the parameters of Eq. (8.3) are random, and the balance between the right and left hand side of (8.3) is ensured by the benefit bt, which will be adjusted accordingly. Indeed, as a result of the linking arrangement, the annuity benefit amount is random, and (part of) the risk is kept by the annuitant. Under a fixed-benefit arrangement, Eq. (8.3) becomes the well-known: b0  axþt1 ð0Þ  ð1 þ iÞ ¼ b0  ð1 þ axþt ð0ÞÞ  pxþt1 ð0Þ,

ð8:4Þ

where the survival probability px + t  1(0) and the actuarial values ax + t  1(0) and ax + t(0) are set at time 0 and guaranteed over the whole life of the annuity. Under a linking arrangement, the survival probability ~pxþt1 can depend on the survival probability observed in a given population. Similarly, the actuarial value of the annuity at time t could be based on an updated best-estimate assumption compared to that adopted at time t  1. If ~pxþt1 ¼ pxþt1 ðτ0 Þ and τ00 ¼ τ0 , the actuarial balance (8.3) is preserved with bt ¼ bt  1. Otherwise, an adjustment of the benefit amount bt compared to bt  1 is required. Alternative choices of the parameters of Eq. (8.3) are discussed in Sect. 8.2.2. Starting from Eq. (8.3), it is useful to obtain an explicit expression for the adjustment coefficient. We can easily write: bt ¼ bt1 

axþt1 ðτ0 Þ  ð1 þ iÞ , ð1 þ axþt ðτ00 ÞÞ  ~pxþt1

ð8:5Þ

where the adjustment coefficient can be interpreted as a ratio between the unitary value of the assets assigned to the policy, namely, ax + t  1(τ0 )  (1 + i), and the unitary value of the policy liability at the end of the year, namely, ð1 þ axþt ðτ00 ÞÞ  ~ pxþt1 . This recalls how benefits are adjusted in self-insured arrangements, such as group self-annuitization plans, where no guarantee is provided. In this case, τ0 ¼ t  1, ½pool ½pool pxþt1 ¼ ~pxþt1 , where ~pxþt1 denotes the survival probability observed τ00 ¼ t and ~ in the pool, i.e. the proportion at time t of survivors in the pool aged x + t  1 at the beginning of the year. For arrangements providing some guarantees, it is convenient to further develop (8.5). First, consider that: axþt1 ðτ0 Þ ¼ ð1 þ axþt ðτ0 ÞÞ  ð1 þ iÞ1  pxþt1 ðτ0 Þ: We can then rearrange Eq. (8.5) as follows:

ð8:6Þ

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bt ¼ bt1 

pxþt1 ðτ0 Þ 1 þ axþt ðτ0 Þ  , ~pxþt1 1 þ axþt ðτ00 Þ

ð8:7Þ

which suggests specific alternative solutions for the adjustment coefficient, that we are going to discuss briefly in Sect. 8.2.2. We note that Eq. (8.7) could be further extended in respect of the interest rate, so as to include financial participation. See Olivieri and Pitacco (2020), where this more general case is presented. Before moving to specific solutions, it is interesting to quote also the case of multi-period benefit adjustments. Considering that the longevity trend can be captured better over a period of several years, rather than year by year, it is reasonable to consider the case of adjustments every k years (say, k ¼ 3 or 5), instead of every year. Olivieri and Pitacco (2020) (to which we refer for details) show that Eq. (8.7) can be generalized as follows: bt ¼ btk 

k pxþtk ðτ

pxþtk k~

0

Þ 1 þ axþt ðτ0 Þ  , 1 þ axþt ðτ00 Þ

ð8:8Þ

where: • It is assumed that the benefit amount is kept unchanged at the level bt  k over the period (t  k, t), while it is subject to an update at time t. • kpx + t  k(τ0) is the probability for an individual age x + t  k to be alive after k years, based on the best-estimate assumption at time τ0 . • k~ pxþtk is the proportion of survivors at age x + t out of a cohort initially aged x + t  k, in a given population. Clearly, with k ¼ 1 we find model (8.7) again.

8.2.2

Particular Solutions

Alternative choices of the parameters of Eqs. (8.7) and (8.8) provide a number of specific solutions. We restrict our attention to the choices that can be interesting for practical purposes, in particular when dealing with insurance products, providing at least partial guarantees. We refer to (8.8), given that (8.7) can be obtained from it, setting k ¼ 1. We first note that a benefit adjustment coefficient involving both the survival probability, p ðτ0 Þ 1þaxþt ðτ0 Þ i.e. the ratio k kxþtk ~ pxþtk , and the actuarial value of the annuity, i.e. the ratio 1þaxþt ðτ00 Þ , would represent a critical choice within an insurance product, since the items subject to change are multiple, making it perhaps difficult to understand the policy conditions by the potential policyholders. Then, we consider only linking arrangements in which p ðτ0 Þ 1þaxþt ðτ0 Þ just either the first ratio, k kxþtk ~ pxþtk , or the second, 1þaxþt ðτ00 Þ, can take a value 6¼ 1. We refer to such choices respectively as a linking by means of the survival probability

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(that we shorten with the label L  SP) and a linking by means of the actuarial value of the annuity (shortened with the label L  AV). Under a L  SP arrangement, we first set ax + t(τ0 ) ¼ ax + t(τ00 ) ¼ ax + t(0), and then Eq. (8.8) reduces to: bt ¼ btk 

k pxþtk ðτ

pxþtk k~

0

Þ

:

ð8:9Þ

The survival probability k ~pxþtk , i.e. the proportion at time t of survivors aged x + t  k at time t  k, can be measured either in the portfolio of the provider or in a ½ptf ½pop reference population; we use, respectively, the notations k ~pxþtk and k ~pxþtk . The former solution, usually referred to as indemnity-based (as it reflects the loss/profit incurred by the provider because of its mortality experience), avoids basis risk for the provider, as the benefits are adjusted in line with the mortality experienced by the provider itself, but it is subject to random fluctuations, due to the (presumably not large) portfolio size. The latter solution, described as index-based (implying the idea of a link to an index), depends on an external experience for the provider. Appropriate choices of the reference population are given, for example, by a representative sample of the population of a country (e.g. Italy) or a region (e.g. England and Wales). A specific cohort (e.g. people born in 1948) can better measure the longevity dynamics. Basis risk for the provider can emerge from an index-based arrangement, due to the possible different dynamics of the mortality recorded by the provider in respect of the mortality referred to for the benefit update. On the other hand, the mortality experienced in the reference population should be less exposed to random fluctuations, due to the larger size when compared to a portfolio; consequently, the benefit amount should be more stable in time. Further, since data are usually collected and processed by an independent institution, individuals should be more confident in the quality of data, and therefore they could be more willing to accept the benefit adjustments. For such reasons, we think that index-based linking arrangements should be adopted in insurance products; indeed, in the following we assume ½pop pxþtk ¼ k ~ pxþtk in (8.9). k~ In Eq. (8.9) the probability kpx + t  k(τ0 ) represents the benchmark survival ½pop probability, which is set at time τ0 , 0
τ0
t  k. If k ~pxþt1 > k pxþtk ðτ0 Þ, the ½pop benefit amount is reduced, while if k ~pxþt1 < k pxþtk ðτ0 Þ , the benefit amount is increased. If the longevity experience of the provider moves in line with the ½pop reference population, whenever k ~pxþt1 > k pxþtk ðτ0 Þ , we should also have ½ptf pxþt1 > k pxþtk ðτ0 Þ, i.e. the provider is incurring into a loss; the reduction of the k~ benefit amount reduces the size of such a loss. A similar but reversed comment can ½pop be made for the case k ~pxþt1 < k pxþtk ðτ0 Þ. If τ0 ¼ 0, the benchmark survival probability is never changed; otherwise, it is updated to more recent mortality projections. In any case, kpx + t  k(τ0 ) must be known at the latest at the beginning of the period, i.e. at time t  k. The choice τ0 ¼ 0 is simpler to explain to the policyholder, as the benchmark survival probability is

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never changed. However, in the case of a mortality trend different from that predicted at time 0, major and iterated adjustments would be necessary. Conversely, when τ0 > 0, the benchmark survival probability is based on a more recent bestestimate assumption, such as the latest projected life table, the processing of which should account for the information gained in the meantime on the mortality trend. p ðτ0 Þ 0 Then, we should expect values for the ratio xþt1 ~ pxþt1 farther from 1 when τ ¼ 0 than when τ0 > 0. Thus, setting τ0 > 0 can respond to the aim of containing the change of the benefit amount, but this obviously has an effect on the premium loading. Further, the choice τ0 > 0 can be harder to explain to the policyholder, as both the numerator and the denominator of the adjustment coefficient are subject to update after the issue of the contract. Let us now discuss a linking by means of the actuarial value of the annuity, that p ðτ0 Þ (as already mentioned) we label L  AV. In this case we set k kxþtk ~ pxþtk ¼ 1, while the actuarial value of the annuity is subject to update. Then, Eq. (8.8) reduces to: bt ¼ btk 

1 þ axþt ðτ0 Þ : 1 þ axþt ðτ00 Þ

ð8:10Þ

If τ00 6¼ τ0 , the actuarial values ax + t(τ0 ) and ax + t(τ00 ) could be based on different best-estimate assumptions (clearly, if such assumptions have changed in the time interval(τ0 , τ00 )). We note that the best-estimate assumptions used to assess actuarial values involve both a discount rate and survival (or death) probabilities; as we mentioned, we disregard financial issues. Thus, we discuss only possible changes of the survival probabilities. If, because of a higher expected lifetime, we find ax + t(τ00 ) > ax + t(τ0), according to (8.10) the benefit amount is reduced. Vice versa, the benefit amount is increased if ax + t(τ00 ) < ax + t(τ0 ). The quantity ax + t(τ0 ) represents the benchmark actuarial value, which should be set at time t  k at the latest, i.e. 0
τ0
t  k. The quantity ax + t(τ00 ) is the actuarial value updated at time τ00 , with 0
τ00
t. As we have noted above for the L  SP arrangement, the choice τ0 ¼ 0 is easier to explain to the policyholder. On the other hand, we could expect more changes in the benefit amounts than in the case τ0 > 0, for reasons similar to those commented above. We point out that a L  AV linking can be considered as an index-based solution. The longevity experience is measured in terms of updated mortality projections. Since they are taken from a life table, the risk that survival probabilities are affected by random fluctuations is low. The confidence of individuals on the quality of the longevity measure should be high, as life tables are usually developed by independent institutions; however, a prediction of future mortality trends is involved, which is exposed to uncertainty risk. We can think of various policy conditions to be underwritten to safeguard the policyholder. For example:

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• Under a L  SP arrangement, appropriate bounds to the survival probability ½pop pxþtk can be used to introduce partial guarantees, to avoid excessive changes of k~ the benefit amount or to avoid the transfer of random fluctuations (indeed, when ½pop pxþt1 6¼ k pxþtk ðτ0 Þ , it is not immediately clear, especially over short time k~ intervals, i.e. when k is small, whether this is due to random fluctuations or systematic deviations; moreover, the insurer is supposed to be able to cover small fluctuations on its own). • Whatever is the arrangement, either L  SP or L  AV, bounds can be set for the benefit amount: bmin
bt
bmax. For example, bmin ¼ 0.75  b0, bmax ¼ 1.25  b0. This provides a guaranteed minimum benefit (bmin); on the other hand, bmax avoids too large increases of the benefit amount, which are not strictly required by the policyholder and could impact negatively on the premium loading. • A maximum age xmax to apply the benefit adjustment can be set bt ¼ bxmax x for t > xmax  x. For example, xmax ¼ 95. This prevents the individual from having to worry about downward fluctuations of the benefit amount at a stage in life where it can be difficult to obtain additional income. • Forms of partial participation could be designed: bt ¼ (1  ψ)  b0 + ψ  bt  k  adj(t  k, t), where ψ represents a participation proportion, 0
ψ
1, which must be chosen at policy issue, while adj(t  k, t) denotes generically the adjustment coefficient. This solution clearly provides a minimum guaranteed benefit, while keeping a form of longevity linking. Some policy conditions overlap: for example, the bounds to the survival probabilities, the bounds to the benefit amount and the partial participation. Clearly, either one or the other should be chosen, as suggested by the features of the longevity index referred to as well as by hedging opportunities or market practice.

8.3 8.3.1

The Valuation of a Longevity-Linked Annuity Portfolio Present Value of Future Benefits and Premium Loading

The (random) present value of future benefits is a basic assessment that must be performed for several purposes. Following Olivieri and Pitacco (2020), we define it at time t, per individual, as follows: ½

PVFBt ¼

ωðxþtÞ X

½

btþh  h ~pxþt  vðt, t þ hÞ,

ð8:11Þ

h¼1 ½

where v(t, t + h) is an appropriate discount factor, while h ~pxþt is measured either in ½ptf ½pop the portfolio (in which case h ~pxþt ) or in a reference population (in which case h ~pxþt ), depending on the purpose of the assessment. In particular, as noted by Olivieri and

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Pitacco (2020), while the probabilities h p~xþt lead to an indemnity-based or entity½pop specific assessment, the probabilities h ~pxþt lead to an index-based valuation. Entityspecific valuations are useful, for example, to perform a realistic assessment of the insurer’s liabilities. Conversely, an index-based valuation avoids accounting for risks which are specific to the insurer (e.g., because of a small portfolio size or a portfolio composition affected by adverse-selection). As we mentioned several times and following Olivieri and Pitacco (2020), we do not address financial issues; therefore, we assume a deterministic value for v(t, t + h), and we do not allow for any financial margin to the insurer, thus we set v(t, t + h) ¼ (1 + i)h. In this regard, we point out that in a deterministic financial setting, no profit should emerge from interest rates. Conversely, an asset management fee is admissible, but we omit it, as it would only produce proportional effects, without significantly affecting the results that we are going to discuss. The present value of future benefits enters the calculation of the premium loading. In this regard, we note that the premium loading should account for the risk-return trade-off of the linking arrangement. This is obtained, for example, following a VaR-like approach. Setting to S the initial amount paid by each policyholder, we assume that the premium loading π must satisfy the following requirement: h i ½pop Pr S < PVFB0 ¼ λ,

ð8:12Þ

where λ (say, λ ¼ 0.1) is the accepted loss probability by the provider. Note that, to avoid charging possible insurer’s inefficiencies to the policyholders, parameters in (8.12) are index-based, so that a basis risk follows for the provider. Given S, the size of λ will impact on the initial benefit amount b0. It is important to point out that the pricing rule (8.12) only focusses on losses; then, consistently, the longevity-linking parameters must be set so to imply a possible participation to losses only (while possible profits are retained by the insurer). The definition of pricing rules in the presence of a participation to both profits and losses is not addressed in this chapter.

8.3.2

Annual Profit and Present Value of Future Profits

The annual profit (or loss) incurred by the provider in year (t  1, t) for a policy in-force at time t can be defined as follows: ½ptf

PLt ¼ V t1 ð1 þ iÞ  ðbt þ V t Þ~pxþt1 ,

ð8:13Þ

where Vt is the individual policy reserve, or technical provision, at time t. We assume that regulation requires that technical provisions are assessed adopting the latest best-estimate assumption, and including a proportional risk margin, in the proportion

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defined by the premium loading π. Thus, we assume that the individual policy reserve at time t is defined as follows: V t ¼ bt  axþt ðt Þ  ð1 þ π Þ:

ð8:14Þ

In particular, V0 ¼ b0  ax(0)  (1 + π) ¼ S. Note that profit is assessed according to an entity-specific logic; this is why when considering in (8.13) the longevity experience of year (t  1, t), we address the ½ptf specific experience of the portfolio (namely, ~pxþt1 ). We also note that there are other quantities that contribute to the annual profit, such as expenses and reinsurance, which we are not going to include in our investigation. Reinsurance, in particular, is outside the scope of this research. It is useful to develop the expression of the annual profit, so to bring out its components. For the individual policy reserve at time t  1, we have: ¼ bt1  axþt1 ðt  1Þ  ð1 þ π Þ

V t1

¼ bt1  ð1 þ axþt ðt  1ÞÞ  pxþt1 ðt  1Þ  ð1 þ iÞ1  ð1 þ π Þ, which, after a slight rearrangement, allows us to express the annual profit as follows: PLt

½ptf

¼ bt1  pxþt1 ðt  1Þ  bt  ~pxþt1   ½ptf þ bt1  axþt ðt  1Þ  pxþt1 ðt  1Þ  bt  axþt ðtÞ  ~pxþt1  ð1 þ πÞ , þbt1  pxþt1 ðt  1Þ  π: ð8:15Þ

In (8.15) we identify three components for the annual profit, namely: • The difference between the expected and the observed benefit payment at the end of the year: ½ben

PLt

½ptf

¼ bt1  pxþt1 ðt  1Þ  bt  ~pxþt1 ¼ bt1  pxþt1 ðt  1Þ 

! ½ptf ~pxþt1 bt 1  ; bt1 pxþt1 ðt  1Þ

ð8:16Þ

• The difference between the expected and the required amount of the reserve at the end of the year:

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A. Olivieri and E. Pitacco ½res

PLt

  ½ptf  ¼ bt1  axþt ðt  1Þ  pxþt1 ðt  1Þ  bt  axþt ðt Þ  e pxþt1  ð1 þ π Þ ½ptf 

e pxþt1 axþt ðt Þ b ¼ bt1  axþt ðt  1Þ  pxþt1 ðt  1Þ  1  t   bt1 axþt ðt  1Þ pxþt1 ðt  1Þ ð1 þ π Þ;

!

ð8:17Þ

• The release of the premium loading: ½load

PLt

½ben

The first component, PLt

¼ bt1  pxþt1 ðt  1Þ  π:

ð8:18Þ

, takes value 0 when bt ¼ bt1  p

½ptf

~ pxþt1

, which can

xþt1 ðt1Þ

½ptf

happen either because this is the linking solution adopted or because p~xþt1 ¼ pxþt1 ðt  1Þ while bt ¼ bt  1. We can comment similarly on the situations in ½res ½load which PLt can take value 0. Conversely, PLt can take value 0 only when π ¼ 0, i.e. when no loading is charged, which is acceptable only if no risk is taken by the provider (as it happens, e.g., in self-insured arrangements). We obtain the present value of future profits at time t (per policy in-force) as follows: PVFPt ¼

ωðxþtÞ X

½ptf

PLtþh  h1 ~pxþt  vðt, t þ hÞ,

ð8:19Þ

h¼1

where, again, v(t, t + h) is an appropriate discount factor. Note that, similarly to the annual profit, the present value of future profits is based on entity-specific assumptions as regards the proportion of survivors. If we set v(t, t + h) ¼ (1 + i)h, as it is reasonable in our financial setting, we find: ½ptf 

PVFPt ¼ V t  PVFBt

:

ð8:20Þ

½ptf 

In particular, PVFP0 ¼ S  PVFB0 . We can obviously assess the present value of each component of the future annual profits, and then we would find: ½ben

PVFPt ¼ PVFPt

½res

þ PVFPt

½load

þ PVFPt

,

ð8:21Þ

where the meaning of the three terms follows clearly from the discussion above. We note that PVFPt is affected by basis risk, due to the mortality assumption adopted for the assessment of the premium loading (see 8.12) and to the indexing rule as well.

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115

Cost of Capital and Business Value

It is a widely accepted idea in economics that the value of a business consists of the present value of future profits net of the cost of capital. In life insurance practice, such an assessment is usually performed with the well-known embedded value methodology, extended along fair valuation principles by the market consistent embedded value; see Blackburn et al. (2017) for an application to life annuities as well as for references. In a market consistent setting, the cost of capital is measured by frictional costs, net of the value of the limited liability put option, which takes a positive value when admitting the possible bankruptcy of the insurer; in such an event, the insurer’s remaining obligations in respect of policyholders would be cancelled, and this originates a value to shareholders. In this paper, we assume that the insurer always has access to the capital needed to fulfil all obligations, and then we accept a value 0 for the limited liability put option. Frictional costs arise from a variety of sources, including taxation and agency costs, as well as the costs of raising capital in the market to recapitalize the insurer. We address frictional cost on shareholder capital arising from the principal-agent problem in the shareholder-management relationship (see Yow and Sherris 2008). The annual frictional cost on shareholder capital is defined as a proportion ρ of the capital held above the technical provision. We assume that the capital size fulfils regulatory requirements; we denote with RCt the required capital at time t for each policy in-force at that time. The annual frictional cost per policy in-force at the beginning of the year is then: FCt ¼ ρ  RCt1 ,

ð8:22Þ

while the corresponding present value at time t, per policy in-force, is obtained as follows: PVFCt ¼

ωðxþtÞ X

½ptf

FCtþh  h1 ~pxþt  vðt, t þ hÞ,

ð8:23Þ

h¼1

where, as previously, we set v(t, t + h) ¼ (1 + i)h. To assess RCt, we stick to the Solvency II principles, according to which the required capital is the amount of funds that the insurer must hold on top of the technical provisions in order to avoid default over 1 year with a 99.5% probability. In respect of longevity risk, this requirement has been reduced to a standard formula which considers the change expected in the net asset value over the year because of a permanent 20% reduction in mortality rates at all ages. While it is claimed that the size of the assumed shock is too high, this standard formula suggests that the possible loss should be measured over a long-term horizon and that an index-

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based assessment should be performed. This is why we obtain the required capital at time t, per policy in-force, from the following condition: h i ½pop Pr RCt þ V t < PVFBt ¼ 0:005

ð8:24Þ

We believe that this way the assessment of the required capital is consistent both with the assumptions of our mortality model and the basic principles of Solvency II. Finally, we define the business value at time t, per policy in-force, as follows: BVt ¼ PVFPt  PVFCt :

8.4 8.4.1

ð8:25Þ

Numerical Implementation Mortality Model

A stochastic mortality model must be adopted, which must be suitable to project mortality at every time (i.e. not just at time 0), to simulate the numbers of survivors and to update the mortality projection as well as the simulated numbers of survivors, according to the gained experience. As is well known, several stochastic mortality models are discussed in the literature. The more sophisticated ensure higher accuracy, but they can present computational complexity. Thus, they are mainly used to perform projections at the initial time only. In view of computational tractability, we prefer to use a simpler model, which quickly updates future forecasts according to the emerging experience. The model is described in Olivieri and Pitacco (2009). With reference to a given cohort consisting of nx individuals at time 0, we assume that the random mortality rate at age x + t, t ¼ 0, 1, . . ., can be expressed as follows: ~qxþt ¼ qxþt ð0Þ  Z xþt ,

ð8:26Þ

where qx + t(0) is the best-estimate mortality rate at time 0 while Zx + t is a (positive) random coefficient (such that 0
~qxþt
1), expressing a deviation of the mortality rate in respect of the best-estimate one, i.e. a deviation in aggregate mortality. We assume:   Z xþt  Gamma αxþt , βxþt , from which it follows:

ð8:27Þ

8 Longevity-Linked Annuities: How to Preserve Value Creation Against Longevity. . .



e qxþt

 βxþt  Gamma αxþt , : qxþt ð0Þ

117

ð8:28Þ

Let nx + t denote the observed number of survivors at age x + t. For the (random) number of deaths at age x + t, Dx + t, we accept the Poisson approximation, given nx + t and conditional on a given value for the mortality rate qx + t: 

  DXþt jqXþt ; nxþt  Poi nxþt  qxþt :

ð8:29Þ

Using (8.26) and (8.27), we obtain a negative binomial unconditional distribution for the number of deaths:  ½DXþt jnxþt   NBin αxþt ,

 θxþt , θxþt þ 1

ð8:30Þ

β

where θxþt ¼ nxþt qxþt ð0Þ. xþt

As far as the parameters in (8.27) are concerned, which drive the aggregate deviations in mortality, we adopt the following inferential procedure. At time 0, when no experience on the cohort is available, we assume: Z xþt  Gammaðα0 , β0 Þ

ð8:31Þ

for all ages x + t, t ¼ 0, 1, . . . . At time 1, a specific information on the mortality of the cohort is gained, namely, the observed number of deaths dx. Then, we can assess the posterior distribution of e qx conditional on the information Dx ¼ dx as follows:   β0 ½e qx jd x   Gamma α0 þ d x , þ nx : qx ð 0Þ

ð8:32Þ

Thanks to (8.26) it then follows: ½Z xþt jdx   Gammaðα1 , β1 Þ,

ð8:33Þ

where α1 ¼ α0 + dx, β1 ¼ β0 + nx  qx(0). These steps can be repeated recursively in time, so that at time h, once the numbers of deaths dx, dx + 1, . . ., dx + h  1 and the numbers of survivors nx, nx + 1 ¼ nx  dx, . . ., nx + h  1 ¼ nx + h  2  dx + h  2 have been observed, the parameters of the probability distribution of Zx + t are updated as follows: αh

¼ α0 þ dx þ dxþ1 þ . . . þ d xþh1 ;

βh

¼ β0 þ nx  qx ð0Þ þ nxþ1  qxþ1 ð0Þ þ . . . þ nxþh1  qxþh1 ð0Þ:

ð8:34Þ

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Note that this way a correlation is (naturally) introduced among the coefficients Zx + t’s. Obviously, at any time h the parameters of the distribution of the number of deaths are also updated. For further details we refer to Olivieri and Pitacco (2009). In this paper, we consider a cohort initial age x ¼ 70. We set α0 ¼ β0, so that at time 0 we have the following expected values for the aggregate deviation in mortality and for the mortality rates: ½Z xþt jF 0  ¼ 1 and ½~qxþt jF 0  ¼ qxþt ð0Þ . We assume that the best-estimate mortality rates at time h are given by ½~ qxþt jF h  ¼ αβh  qxþt ð0Þ, where the parameters αh, βh are updated to the mortality h observed (i.e. simulated) in a reference (large) population. The best-estimate mortality rates qx + t(0) are obtained from a Gompertz law with parameters as in Bacinello et al. (2018). The expected lifetime at age 70 is roughly 15 years; to avoid the impact of major random fluctuations at the highest ages, the maximum age is set at 100. Two alternative values for α0 are considered: α0 ¼ 100, α0 ¼ 1000, expressing (respectively) a major and a moderate aggregate longevity risk (at time 0, the coefficient of variation of Zx + t is 0.1 in the former case, 0.0316 in the latter). Basis risk is included by addressing a portfolio with a much reduced size in respect of the reference population; otherwise, the portfolio follows the same trend as the reference population (which means that basis risk is only attributable to random fluctuations).

8.4.2

Benefit Structures Examined

We consider the following benefit structures: 1. Fixed benefit (FB): bt ¼ b0. 2. Linking by means of the survival probability, with benchmark probability set at ð0Þ time 0 (L-SP(0)): bt ¼ b0  t px½pop . px te 3. Linking by means of the survival probability, with benchmark probability set at p ðtkÞ time at time t  k (L-SP(t  k)): bt ¼ btk  k xþtk . ½pop pxþtk k~

4. Linking by means of the actuarial value of the annuity, with benchmark life table xþt ð0Þ set at time 0, to be compared to the latest life table (L-AV(0, t)): bt ¼ b0  1þa 1þaxþt ðt Þ. 5. Linking by means of the actuarial value of the annuity, with benchmark life table set at time t  k, to be compared to the latest life table (L-AV(t  k, t)): bt ¼ xþt ðtk Þ btk  1þa 1þaxþt ðt Þ .

Arrangement 1, in particular, is used as a reference to interpret the results of the linking solutions. For arrangements 2–5 some guarantees are introduced: a minimum benefit amount, namely, 0.75  b0
bt
b0, and a maximum age for the benefit adjustment, xmax ¼ 95. Further, annual or multi-period adjustments are considered, assuming alternatively k ¼ 3 or k ¼ 5 in the latter case. We think that this way we

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119

have a fairly significant comparison of a number of possible linking solutions that could be adopted in insurance products.

8.4.3

Numerical Assessments and Discussion

We perform an assessment of the quantities described in Sect. 8.3, namely, the premium loading, the present value of future profits (split into its components) and the business value, including basis risk or not. We also quote the size of the required capital. Three valuation times are included, namely, time 0, time 10 and time 20, so to have an insight into the time profile of the business value. We note that from the present value of future profits at time 0 we obtain information also about the initial ½ptf  present value of future benefits, as PVFP0 ¼ S  PVFB0 (see 8.20). All assessments are performed simulating the numbers of survivors in a reference (large) population and in an annuity portfolio. The mortality experience in the reference population is also used to update the best-estimate life table at all times. As we have already mentioned, we disregard financial risk and we adopt a deterministic financial setting. Given that at present risk-free rates are very low, we set a 0 interest rate at any time. Obviously, present values are affected by this choice, but only in a deterministic way. Taking a positive (but still deterministic) interest rate would not affect significantly the main conclusions of the numerical assessment. The proportion of frictional costs is set to ρ ¼ 2%, following market practice (see Blackburn et al., 2017). Table 8.1 quotes the quanty π for the alternative linking arrangements, that is the premium loading, which has been assessed adopting an accepted loss probability λ ¼ 0.1. It is useful to recall that the case of moderate longevity risk is represented by Table 8.1 Premium loading π ¼ b0 aSx ð0Þ  1 , ensuring an accepted 0.1 loss probability to the provider Arrangement FB L-SP(t  k), k ¼ 1 L-SP(t  k), k ¼ 3 L-SP(t  k), k ¼ 5 L-AV(t  k, t), k ¼ 1 L-AV(t  k, t), k ¼ 3 L-AV(t  k, t), k ¼ 5 L-SP(0), k ¼ 1 L-SP(0), k ¼ 3 L-SP(0), k ¼ 5 L-AV(0, t), k ¼ 1 L-AV(0, t), k ¼ 3 L-AV(0, t), k ¼ 5

Moderate longevity risk (%) 1.945 1.839 1.702 1.567 0.124 0.276 0.444 0.070 0.303 0.475 0.001 0.146 0.373

Major longevity risk (%) 6.352 6.088 5.591 5.135 0.325 0.836 1.379 0.226 0.960 1.497 0.062 0.419 1.166

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setting α0 ¼ 1000 in the probability distribution describing the possible deviations in mortality rates, while α0 ¼ 100 represents the case of major longevity risk. Table 8.1 provides some interesting information about the sharing of longevity risk between the provider and the individual implied by the different adjustment arrangements. First, it emerges that, as we should expect, the loadings are higher when major aggregate deviations in mortality are expected. Second, we see that the premium loading is a result of the extent of the possible benefit adjustment. In particular, a lower premium loading is required when the benefit is more reactive to an unfavourable longevity experience. Fixed benefits require the highest loading, given that the risk is fully retained by the insurer. In the other cases, from the size of the premium loading we get an idea of how much the adjustment coefficient reacts to unfavourable longevity dynamics. The loading required for the arrangement L-SP(t  k), k ¼ 1 is similar to the case of fixed benefits. This is because both the numerator and the denominator of the ratio pxþt1 ðt1Þ reflect the same experience, whence the value taken by the ratio itself ½pop ~pxþt1

should be close to 1. While this result is a consequence of the mortality model, it is reasonable that it holds also, in general, whatever is the model. This is due to the fact that both the latest best-estimate assumptions and the current number of survivors reflect the latest observed mortality trends. We see also that there is a trade-off between the frequency of benefit adjustment and the time at which the benchmark assumption is latest updated. For example, under arrangement L-SP(t  k) the loading decreases if k > 1; indeed, in this case the numerator of the adjustment coefficient, namely, kpx + t  k(t  k), is updated to the experience k years ago (k > 1), ½pop while the denominator, namely, k ~pxþtk, comes from the experience until the current time. Thus, we should expect that the ratio between the two probabilities is less close to 1 than in the case k ¼ 1. In the arrangements L-SP(0) and L-AV(0, t), where the benchmark best-estimate assumption is defined at time 0, it is more probable (than in the cases in which the benchmark is chosen at time t  k) that the adjustment coefficient takes a value far from 1. As a consequence, the loadings required for such arrangements are lower. The arrangement L-AV(0, t) requires the lowest loadings (even negative); this is because actuarial values are aggregate values and, differently from survival probabilities, they account already for an expected change in the future mortality trend, based on the recent experience. If the benchmark best-estimate assumption is defined at time 0, the premium loading is lower when k > 1, due to the reduced frequency of adjustment. Similarly to what commented for L-SP(t  k), if the benchmark bestestimate is updated, such as for arrangements L-SP(t  k) and L-AV(t  k, t), there is a trade-off between the frequency of adjustment and the distance between the reference information in the numerator and the denominator. For arrangement L-SP(t  k), the trade-off is dominated by the latter aspect; for arrangement L-AV (t  k, t), the former prevails. Arrangement L-AV(t  k, t) requires a higher loading than L-AV(0, t); this is because of the different distance between the reference information in the numerator and the denominator. The lower magnitude of the loading for arrangement L-AV

8 Longevity-Linked Annuities: How to Preserve Value Creation Against Longevity. . .

121 ½ben

Table 8.2 Expected value of the present value of future profits (PVFP0), its components (PVFP0 ½res PVFP0

,

½load PVFP0 )

and and the business value (BV0), at time 0. Values of the expected value of PVFP0 are quoted per policy issued and for an initial amount S ¼ 100; the other quantities are quoted as a % of the expected value of PVFP0. Moderate longevity risk, no basis risk Arrangement FB L-SP(t  k), k ¼ 1 L-SP(t  k), k ¼ 3 L-SP(t  k), k ¼ 5 L-AV(t  k, t), k ¼ 1 L-AV(t  k, t), k ¼ 3 L-AV(t  k, t), k ¼ 5 L-SP(0), k ¼ 1 L-SP(0), k ¼ 3 L-SP(0), k ¼ 5 L-AV(0, t), k ¼ 1 L-AV(0, t), k ¼ 3 L-AV(0, t), k ¼ 5

PVFP0 1.884 1.844 1.745 1.650 0.747 0.800 0.898 0.630 0.788 0.904 0.615 0.687 0.837

½ben

PVFP0 0.235 0.073 0.190 0.392 4.946 4.226 3.578 10.227 7.226 5.989 5.649 4.934 3.766

(%)

½res

PVFP0 (%) 1.062 2.028 3.943 6.172 78.589 61.576 47.411 78.737 54.580 41.960 94.117 73.876 52.055

½load

PVFP0 101.297 97.899 95.867 93.436 16.465 34.198 49.011 11.036 38.194 52.051 0.234 21.191 44.179

(%)

BV0 (%) 79.479 79.948 80.571 81.103 96.617 93.293 90.400 97.773 92.438 89.771 97.206 95.657 91.279

½ben

Table 8.3 Expected value of the present value of future profits (PVFP0), its components (PVFP0 ½res PVFP0

,

½load PVFP0 )

and and the business value (BV0), at time 0. Values of the expected value of PVFP0 are quoted per policy issued and for an initial amount S ¼ 100; the other quantities are quoted as a % of the expected value of PVFP0. Major longevity risk, no basis risk Arrangement FB L-SP(t  k), k ¼ 1 L-SP(t  k), k ¼ 3 L-SP(t  k), k ¼ 5 L-AV(t  k, t), k ¼ 1 L-AV(t  k, t), k ¼ 3 L-AV(t  k, t), k ¼ 5 L-SP(0), k ¼ 1 L-SP(0), k ¼ 3 L-SP(0), k ¼ 5 L-AV(0, t), k ¼ 1 L-AV(0, t), k ¼ 3 L-AV(0, t), k ¼ 5

PVFP0 5.749 5.620 5.315 5.035 2.055 2.342 2.682 1.886 2.380 2.727 1.800 2.042 2.531

½ben

PVFP0 0.178 0.040 0.131 0.355 5.300 4.487 3.807 10.625 7.546 6.230 6.215 5.366 4.023

ð%Þ

½res

PVFP0 3.961 2.193 0.258 2.784 79.178 60.631 46.111 77.607 53.017 40.258 97.177 74.501 51.035

(%)

½load

PVFP0 104.139 102.233 99.611 96.861 15.523 34.882 50.082 11.768 39.438 53.512 3.392 20.132 44.942

(%)

BV0 (%) 78.399 78.789 79.327 79.898 96.795 93.301 90.523 85.467 85.864 84.570 98.645 95.377 90.907

(t  k, t) when compared to L-SP(t  k) can be justified by the fact that actuarial values are aggregate values and (as already noted) they account already for an expected change in the future mortality trend as suggested by the recent experience.

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Table 8.4 Required capital at time 0, as a % of the technical provision Arrangement FB L-SP(t  k), k ¼ 1 L-SP(t  k), k ¼ 3 L-SP(t  k), k ¼ 5 L-AV(t  k, t), k ¼ 1 L-AV(t  k, t), k ¼ 3 L-AV(t  k, t), k ¼ 5 L-SP(0), k ¼ 1 L-SP(0), k ¼ 3 L-SP(0), k ¼ 5 L-AV(0, t), k ¼ 1 L-AV(0, t), k ¼ 3 L-AV(0, t), k ¼ 5

Moderate longevity risk (%) 1.961 1.876 1.720 1.582 0.129 0.273 0.439 0.071 0.303 0.470 0.088 0.152 0.372

Major longevity risk (%) 6.270 6.025 5.560 5.127 0.339 0.805 1.300 1.399 1.715 2.141 0.126 0.484 1.177

Tables 8.2 and 8.3 quote the present value of future profits at time 0 (per policy issued and for an initial amount S ¼ 100 monetary units), the expected value of its components and the expected value of the business value at time 0; to ease comparison, the latter quantities are quoted as a % of the expected value of PVFP0. In the assessment of PVFP0 we include no basis risk, by assuming that the mortality in the ½ptf  portfolio is exactly the same as in the reference population, so that PVFB0 ¼ ½pop PVFB0 . In general, the magnitude of the expected present value of future profits, and the other quantities as well, is highly affected by the size of the premium loadings (which are those of Table 8.1. In effect, in Tables 8.2 and 8.3, we find results in line with those of Table 8.1. However, the size of the business value per unit of present value of future profits is not in line with the premium loading. This is because the business value is also affected by the cost of capital. Such a quantity depends, in particular, on the size of the required capital, which is higher when the risk-profile for the provider is more severe. Thus, in relative terms, the value created by a fixed-benefit arrangement is lower than for longevity-linked arrangements. Further, in relative terms the business value is higher when the benefit is more reactive to an adverse experience. To provide a more detailed idea of the size of the required capital, Table 8.4 quotes the average proportion of the required capital per unit of technical provision at time 0; the lower such a proportion, the lower the riskiness retained by the provider. The components of the present value of future profits in Tables 8.2 and 8.3 highlight the features of the longevity-linking arrangement, in particular as far as ½ben ½res the components PVFP0 and PVFP0 are concerned. Clearly, under L-SP arrangements, the former component is larger than the latter, and the reverse applies to ½load L-AV arrangements. Finally, PVFP0 reflects the size of the premium loading. Tables 8.5 and 8.6 quote the same quantities as Tables 8.2 and 8.3, but we have included basis risk, by assuming that because of a lower portfolio size, the mortality

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123 ½ben

Table 8.5 Expected value of the present value of future profits (PVFP0), its components (PVFP0 ½res PVFP0

,

½load PVFP0 )

and and the business value (BV0), at time 0. Values of the expected value of PVFP0 are quoted per policy issued and for an initial amount S ¼ 100; the other quantities are quoted as a % of the expected value of PVFP0. Moderate longevity risk, basis risk Arrangement FB L-SP(t  k), k ¼ 1 L-SP(t  k), k ¼ 3 L-SP(t  k), k ¼ 5 L-AV(t  k, t), k ¼ 1 L-AV(t  k, t), k ¼ 3 L-AV(t  k, t), k ¼ 5 L-SP(0), k ¼ 1 L-SP(0), k ¼ 3 L-SP(0), k ¼ 5 L-AV(0, t), k ¼ 1 L-AV(0, t), k ¼ 3 L-AV(0, t), k ¼ 5

PVFP0 1.859 1.818 1.718 1.622 0.711 0.763 0.861 0.581 0.742 0.859 0.577 0.649 0.800

½ben

PVFP0 1.467 1.183 1.146 1.030 1.765 1.222 0.887 6.414 4.079 3.202 1.759 1.429 0.877

½res

PVFP0 1.228 1.890 3.779 5.975 80.945 62.952 48.017 81.628 55.350 42.019 97.991 76.149 52.882

(%)

½load

PVFP0 102.696 99.294 97.367 95.055 17.290 35.826 51.096 11.958 40.571 54.779 0.250 22.422 46.241

ð %Þ

BV0 (%) 79.198 79.665 80.269 80.778 96.448 92.975 89.993 97.587 91.969 89.237 97.024 95.405 90.873

½ben

Table 8.6 Expected value of the present value of future profits (PVFP0), its components (PVFP0 ½res PVFP0

,

½load PVFP0 )

and and the business value (BV0), at time 0. Values of the expected value of PVFP0 are quoted per policy issued and for an initial amount S ¼ 100; the other quantities are quoted as a % of the expected value of PVFP0. Major longevity risk, basis risk Arrangement FB L-SP(t  k), k ¼ 1 L-SP(t  k), k ¼ 3 L-SP(t  k), k ¼ 5 L-AV(t  k, t), k ¼ 1 L-AV(t  k, t), k ¼ 3 L-AV(t  k, t), k ¼ 5 L-SP(0), k ¼ 1 L-SP(0), k ¼ 3 L-SP(0), k ¼ 5 L-AV(0, t), k ¼ 1 L-AV(0, t), k ¼ 3 L-AV(0, t), k ¼ 5

PVFP0 5.700 5.569 5.254 4.963 1.904 2.187 2.525 1.635 2.147 2.506 1.636 1.878 2.371

½ben

PVFP0 1.436 1.333 1.267 1.156 1.110 0.781 0.557 5.607 3.441 2.598 1.434 1.113 0.602

ð %Þ

½res

PVFP0 ð%Þ 3.562 1.817 0.509 2.876 82.123 61.835 46.216 80.792 52.778 39.101 102.299 76.975 51.384

½load

PVFP0 104.998 103.150 100.758 98.279 16.768 37.384 53.227 13.601 43.781 58.301 3.733 21.912 48.013

ð%Þ

BV0 (%) 78.247 78.624 79.114 79.629 96.543 92.831 89.942 83.238 84.338 83.222 98.510 94.976 90.300

in the portfolio is not exactly the same as in the reference population. Basis risk is therefore simply attributable to random fluctuations. Results are in line with the previous ones, with lower expected values, due to basis risk.

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Table 8.7 Expected value of the present value of future profits (PVFPt) and the business value (BVt), at time t ¼ 10 and t ¼ 20. Values of the expected value of PVFPt are quoted per policy in-force and for an initial amount S ¼ 100; the expected business value is quoted as a % of the expected value of PVFPt. Moderate longevity risk, no basis risk Arrangement FB L-SP(t  k), k ¼ 1 L-SP(t  k), k ¼ 3 L-SP(t  k), k ¼ 5 L-AV(t  k, t), k ¼ 1 L-AV(t  k, t), k ¼ 3 L-AV(t  k, t), k ¼ 5 L-SP(0), k ¼ 1 L-SP(0), k ¼ 3 L-SP(0), k ¼ 5 L-AV(0, t), k ¼ 1 L-AV(0, t), k ¼ 3 L-AV(0, t), k ¼ 5

PVFP10 1.123 1.083 0.999 0.917 0.068 0.160 0.257 0.449 0.550 0.568 0.011 0.084 0.209

BV10 (*) (%) 80.676 80.286 79.890 80.010 82.704 36.347 69.016 94.708 99.393 100.629 975.641 2.739 67.813

PVFP20 0.534 0.515 0.475 0.432 0.020 0.063 0.111 0.187 0.285 0.223 0.047 0.001 0.070

BV20 (*) (%) 87.021 86.589 86.124 85.775 136.456 35.980 68.509 85.993 94.127 91.341 207.737 3185.492 44.465

Finally, Table 8.7 quotes the present value of future profits and the business value, after 10 and 20 years, considering a moderate longevity risk and no basis risk. Comparing this Table with Table 8.2, an information about the timing of the release of value can be obtained. We note, in particular, that arrangements L-AV imply that most of the value is released at issue, which is due to the aggregate nature of the quantities involved by the adjustment coefficient. On the contrary, in the other situations, value seems to be more gradually released in time. For brevity, we omit to show the cases of major longevity risk and basis risk, under which (apart from the magnitude of the values) we obtain similar comparisons. The time profile of the release of the value is an important issue in the decision of the insurer about which annuity design should be preferred, in view of the risk-return trade-off as well as the disclosure of value creation.

8.5

Concluding Remarks

This chapter addresses annuity designs in which the benefit amount is updated to the mortality experience. Linking the annuity benefit amount to the mortality experience implies a new definition of the longevity guarantee, which can be convenient both to the individual and the provider. Individuals, in particular, can benefit from a reduction in premium loadings. Providers, conversely, do not need to underwrite a fixed guarantee in respect of a long-term risk, which is difficult to predict. Understanding the risk-return trade-off of a new design of the longevity guarantees is important, from both the point of view of individuals and providers.

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In this chapter, we focus in particular on the point of view of the provider. We investigate the business value, measuring it as the present value of future profits net of the cost of capital. This latter quantity is assessed in a market-consistent way, namely, in terms of frictional costs. The capital size is defined explicitly accounting for the risk retained by the provider. This way, we are able to account for the riskreturn trade-off for the provider when assessing the business value. We also address the time-profile of the business value. The numerical assessments suggest that a linking solution involving a comparison between the survival probability based on best-estimate assumptions at time 0 and the observed proportion of survivors could be an appropriate design from the point of view of the provider. Further research should now be developed taking the point of view of the individual. The investigation can be carried out further following several lines of study. From the point of view of the individual, the comparison of the several solutions could be developed modelling individual’s preferences. From the point of view of the provider, the assessment of the business value can be analysed more in-depth, addressing the demand function, and then allowing also for the limited liability put option. The case of multiple cohorts should also be examined. A topic which deserves further research is the pricing of the guarantees. It is worth developing pricing models of the options embedded in the arrangements, admitting a participation also to possible profits. The possibility to introduce flexibility by charging annual fees for the guarantees is also a solution which could be explored. Finally, the joint presence of financial and longevity linking is a problem of theoretical and practical importance.

References Bacinello, A. R., Millossovich, P., & Chen, A. (2018). The impact of longevity and investment risk on a portfolio of life insurance liabilities. European Actuarial Journal, 8(2), 257–290. Baker, T., & Siegelman, P. (2010). Tontines for the invincibles: Enticing low risks into the health insurance pool with an idea from insurance history and behavioral economics. Wisconsin Law Review, 2010, 79–120. Blackburn, C., Hanewald, K., Olivieri, A., & Sherris, M. (2017). Longevity risk management and shareholder value for a life annuity business. ASTIN Bulletin, 47(1), 43–77. Blake, D., Cairns, A. J. G., Dowd, K., & Kessler, A. R. (2019). Still living with mortality: The longevity risk transfer market after one decade. British Actuarial Journal, 24, 1–80. Bravo, J. M., & de Freitas, N. E. M. (2018). Valuation of longevity-linked life annuities. Insurance: Mathematics & Economics, 78, 212–229. Chen, A., & Rach, M. (2019). Options on tontines: An innovative way of combining tontines and annuities. Available at SSRN. 10.2139/ssrn.3398144 Chen, A., Hieber, P., & Klein, J. K. (2019). Tonuity: A novel individual-oriented retirement plan. ASTIN Bulletin, 49(1), 5–30. Denuit, M., Haberman, S., & Renshaw, A. (2011). Longevity-indexed life annuities. North American Actuarial Journal, 15(1), 97–111. Denuit, M., Haberman, S., & Renshaw, A. (2015). Longevity-contingent deferred life annuities. Journal of Pension Economics and Finance, 14(03), 315–327.

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Donnelly, C. (2015). Actuarial fairness and solidarity in pooled annuity funds. ASTIN Bulletin, 45 (01), 49–74. Donnelly, C., Guillén, M., & Nielsen, J. P. (2013). Exchanging uncertain mortality for a cost. Insurance: Mathematics & Economics, 52, 65–76. Donnelly, C., Guillén, M., & Nielsen, J. P. (2014). Bringing cost transparency to the life annuity market. Insurance: Mathematics & Economics, 56, 14–27. Lüthy, H., Keller, P. L., Bingswanger, K., & Gmür, B. (2001). Adaptive algorithmic annuities. Mitteilungen der Schweizerischen Aktuarvereinigung, 2, 123–138. Maurer, R., Mitchell, O. S., Rogalla, R., & Kartashov, V. (2013). Lifecycle portfolio choice with systematic longevity risk and variable investment-linked deferred annuities. The Journal of Risk and Insurance, 80(3), 649–676. McKeever, K. (2009). A short history of tontines. Fordham Journal of Corporate and Financial Law, 15(2), 491–521. Milevsky, M. A. (2014). Portfolio choice and longevity risk in the late seventeenth century: A re-examination of the first English tontine. Financial History Review, 21(3), 225–258. Milevsky, M. A., & Salisbury, T. S. (2015). Optimal retirement income tontines. Insurance: Mathematics & Economics, 64, 91–105. Milevsky, M. A., & Salisbury, T. S. (2016). Equitable retirement income tontines: Mixing cohorts without discriminating. ASTIN Bulletin, 46(3), 571–604. Olivieri, A., & Pitacco, E. (2009). Stochastic mortality: The impact on target capital. ASTIN Bulletin, 39(2), 541–563. Olivieri, A., & Pitacco, E. (2015). Introduction to insurance mathematics. Technical and financial features of risk transfers (2nd ed.). Cham: Springer. Olivieri, A., & Pitacco, E. (2020). Linking annuity benefits to the longevity experience: Alternative solutions. Annals of Actuarial Science, 1–22. https://doi.org/10.1017/S1748499519000137 Piggott, J., Valdez, E. A., & Detzel, B. (2005). The simple analytics of a pooled annuity fund. The Journal of Risk and Insurance, 72(3), 497–520. Qiao, C., & Sherris, M. (2012). Managing systematic mortality risk with group self-pooling and annuitization schemes. The Journal of Risk and Insurance, 80(4), 949–974. Richter, A., & Weber, F. (2011). Mortality-indexed annuities. Managing longevity risk via product design. North American Actuarial Journal, 15(2), 212–236. Sabin, M.J. (2010). Fair tontine annuity. Available at SSRN.com http://ssrn.com/ abstract¼1579932 Stamos, M. Z. (2008). Optimal consumption and portfolio choice for pooled annuity funds. Insurance: Mathematics & Economics, 43, 56–68. Valdez, E. A., Piggott, J., & Wanga, L. (2006). Demand and adverse selection in a pooled annuity fund. Insurance: Mathematics & Economics, 39, 251–266. Weinert, J. H. & Gründl, H. (2016). The modern tontine: An innovative instrument for longevity risk management in an aging society (Working paper series 22/2016), ICIR Yow, S., & Sherris, M. (2008). Enterprise risk management, insurer value maximisation, and market frictions. ASTIN Bulletin, 38(1), 293–339.

Chapter 9

Modelling the Life Expectancy of Elderly People for Life Insurance and Pension Systems Anna Jędrzychowska and Jan Gogola

9.1

Introduction

The longevity risk, which is the risk that people will live longer than expected, weighs heavily on those who run pension schemes and on the insurers that provide annuities. This risk is also essential for households planning to save for retirement. Thus, the correct prediction of future mortality rates is an issue of fundamental importance for insurance companies, pensions industry, and households. There are a growing number of people surviving to retirement age, as well as an extended period in which pension annuity providers need to pay out benefits. Therefore, improving mortality rates will have a direct effect on the present value of future liabilities and the related level of reserves held by institutions paying these benefits. A reliable estimation of mortality rates in future years is therefore essential. We therefore conducted an analysis that focused on mortality at higher ages (65–95), which is in line with the current interest in pension-related applications where the risk associated with longer-term cash flow is primarily linked to uncertainty regarding future rates of mortality. The longevity risk has an influence on the reserves established for the payment of benefits (retirement, widowhood, orphanhood, disability, dependency), which will be inadequate if they are based on typical life tables (or mortality tables) as these suggest a lower survival rate than is in fact the case. In the Solvency II framework, the longevity risk is a sub-module of the underwriting risk module. This risk plays a central role in the financial management of insurance companies as only careful assumptions about the future evolution of mortality will enable the company to fulfil its future obligations. A. Jędrzychowska (*) Wroclaw University of Economics and Business, Wroclaw, Poland e-mail: [email protected] J. Gogola University of Pardubice, Pardubice, The Czech Republic e-mail: [email protected] © Springer Nature Switzerland AG 2020 M. Borda et al. (eds.), Life Insurance in Europe, Financial and Monetary Policy Studies 50, https://doi.org/10.1007/978-3-030-49655-5_9

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Long-standing observations of the characteristics of survival distribution in the human population show that these change over time. For instance, annual probabilities of death in developed countries have been decreasing in recent decades. Furthermore, these changes are irregular; thus, the probabilities can be viewed as processes that are characterised by specific stochastic variability as well as a general tendency (Wilmoth and Horiuchi 1999). The mortality of populations in developed countries has also improved rapidly over the last 30 years (see Table 9.1 in Appendix—Kappa t). This situation has important financial implications for the insurance industry and households, because several important classes of liability are sensitive to the direction of future mortality trends. This uncertainty about the future development of mortality creates a longevity risk and requires a more effective tool to predict mortality risk: cohort life tables. Complementing the motivation to address this topic is a sentence from the article “Longevity swaps: Live long and prosper” (The Economist 2010): “Every additional year of life expectancy at age 65 is reckoned to bump up the present value of pension liabilities in British defined-benefit schemes by 3%, or GBP 30 billion (USD48 billion)”. This statement shows how correct estimation of the life expectancy of beneficiaries has a considerable impact on the pension system. We therefore decided to estimate the effect of an increase in life expectancy on the present value of annuities in selected European countries. The calculations provide an illustration of the theoretical considerations needed for these countries. These included two countries with the highest level of life expectancy in Europe (Sweden and France), two countries with the lowest life expectancy (Lithuania and Latvia), the countries of origin of the authors (the Czech Republic, Slovakia, and Poland), and Hungary (in this country both authors had the pleasure of working at the University of Budapest). Figure 9.1 provides evidence to show that life expectancy in the selected European countries has been increasing over the last decades. The main goal of this chapter is to apply the Lee-Carter model to construct “cohort life tables” and use them to calculate a 30-year annuity for a person aged 85 80

75 70 65 60 1950

1955

1960

CZ

1965

1970

FR

1975

HU

1980

LT

1985

1990

LV

1995

PL

2000

SE

2005

2010

2015

SK

Fig. 9.1 Life expectancy at birth for persons in selected countries, 1950–2017. Source: Human Mortality Database—HMD (www.mortality.org)

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65 in 2015. The selected European countries will then be used to illustrate differences resulting from the use of two types of life expectancy table. Finally, the authors emphasise the importance of using cohort tables as these are sensitive to changing demographic trends and provide greater financial security for retirement plans. Additionally, the article shows that countries with the lowest life expectancy (developing countries) should definitely operate their pension system based on cohort life tables. They are currently experiencing the strongest demographic changes and a failure to change pension arrangements to accommodate the fact that citizens are living longer may threaten the stability of the pension system.

9.2

Literature Review

There is considerable debate regarding the extent of increases in longevity. While some argue that there are no limits to life expectancy (e.g., Oeppen and Vaupel 2002), others are more conservative (e.g. Olshansky et al. 2005; Allison and Ludwig 2005). The first group draw their conclusions from historical trends and age trajectories. They argue that mortality is likely to level off after some (unspecified) threshold and, consequently, longevity will be uncapped and will continue to increase over the next few decades. However, this claim remains controversial. The more conservative group argues that an epidemiological transition, as well as the incerasing in mortality rates required to produce even small increases in life expectancy, means that increases in life expectancy will slow down if not stop altogether (Olshansky et al. 2005). They believe that the human lifespan might have natural limits (Antolin 2007). Recalling the Antolin’s (2007) study published in the OECD report, it is emphasised that empirical evidence showing that survival probability curves have become increasingly rectangular or compressed (Kannisto 2000) also suggests there are limits to life expectancy. Regrettably, this compression of mortality theory is inconclusive (Siegel 2005). Consequently, there is a large degree of uncertainty surrounding future improvements in mortality and also life expectancy, especially at older ages. This uncertainty means that a different approach needs to be adopted to model future improvements and, in particular, to assess the uncertainty surrounding these improvements. In this context, it is better to use a stochastic approach rather than deterministic to forecast improvements because this allows probabilities to be attached to a full range of different outlooks and will facilitate adequate assessment of both uncertainty and risks. According this work (Antolin 2007), the mortality rates have declined steadily over the past century, which translates into significant increases in life expectancy at both birth and age 65. These declines stem from substantial reductions in mortality rates among younger generations and, to some extent, improvements among those of older ages. During the first part of the twentieth century, the decline in mortality was mainly caused by a reduction in infectious diseases (especially important for younger people). But during the last

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decades of the twentieth century, the decline in mortality was due to reductions in deaths caused by chronic diseases (important for older generations). The presented by international organizations and national statistical institutes projections for the next 50 years indicate a slower reduction in mortality and less tendency to lengthen life expectancy than in the recent past. Future projections assume that the projected gains in life expectancy at birth for the next 50 years will slow by almost half in comparison with the benefits experienced in the second half of the last century. Future increases in life expectancy will have to come primarily from further decreases in mortality rates at older ages. As OECD report (Antolin 2007) mentions given the lack of sufficient data, estimating and forecasting mortality rates and life expectancy for the very old (those aged 85 or more) is challenging. Data for the very old are not extremely accurate because the samples are often small. It is commonly accepted that from the ages of 30–85, age-specific death rates tend to rise at an approximately fixed rate of increase. This rate of growth tends to fall for those above the age of 85 (Robine and Vaupel 2002; Wilmoth and Horiuchi 1998). As emphasised by scientists modelling life expectancy, it is therefore a certain degree of uncertainty about the extent of future improvements in mortality rates and life expectancy. There are different views regarding the outlook for human longevity (Siegel 2005). Nevertheless, because mortality rates among young and middle-aged people have reached extremely low levels, improvements would have to come from a decline in mortality at older ages, which essentially means an increase in life expectancy at age 65 or more and, in particular, at very old ages (85+). The first attempt to mathematically model the intensity of deaths in dynamic terms was undertaken by Blaschke (1923) who utilised the so-called dynamic Makeham’s law. His model assumes that the mortality intensity μx(t) is the function not only of age x but also of calendar time t. Life expectancy is also a common statistical measure of the average remaining time an individual is expected to live given his/her current age, year of birth, sex, and other demographic and socioeconomic factors such as education, income, and occupation (Ayuso et al. 2017). To compute life expectancy, the standard procedure involves building an ordinary life table, which is a tabular statistical tool that summarises the survival and mortality experiences of a population and yields further understanding of longevity prospects. In the past, analytical methods and mortality laws (e.g. De Moivre, Gompertz, Makeham, Weibull) have been used to compute life expectancy estimates (Antolin 2007). An extensive review of mortality forecasting methods can be found in Bravo (2007), Booth and Tickle (2008), and Blake et al. (2017). The stochastic character of mortality-related processes justifies the need to adopt stochastic methods to model, forecast, and describe these phenomena. Ronald D. Lee and Lawrence Carter developed the Lee-Carter model in 1992. The model grew out of their work in the late 1980s and early 1990s, when they attempted to use inverse projection to infer rates from historical demography. The model has since been used by the US Social Security Administration, the US Census Bureau, and the United Nations and has become the most widely used mortality forecasting technique in the world.

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This model applies the theory of random walk with a drift to modelling and forecasts the crude age-specific death rates mx(t), cross-classified by age x and by the calendar year t. In current simulations of mortality risk, it is necessary to dispense with period life tables and use dynamic tables that are fitted to historical data where one or more time-varying parameters are identified. By extrapolating these parameters to the future, it is possible to obtain a forecast of future death probabilities and other demographic quantities such as life expectancies. These are essential for quantifying longevity in pension risks and for constructing benchmarks for longevity-linked liabilities. The concept of using cohort life expectancy tables was outlined in the earlier work of the authors—Gogola (2014a, b, c, 2015), Gogola and Slavíček (2016), Jindrová and Slavíček (2012), Pacáková and Jindrová (2014), and Pacáková et al. (2013).

9.3

Methodology and Data

This research used data on total population deaths and exposure to risk between 1950 and 2014 for the selected countries (except for Poland, where data were only available for 1958, and Lithuania and Latvia, where data were only available from 1959). The data were obtained from the Human Mortality Database (www.mortality. org). It was assumed that the restricted age range was from 0 to 95. In this model, calendar year t runs from exact time t to exact time t + 1 and dx, t denotes the number of deaths aged x last birthday in the calendar year t. It is assumed that the data on deaths are arranged in a matrix D ¼ (dx, t). Similarly, the data on exposure are arranged in a matrix Ec ¼ (ex, t) where ex, t is a measure of the average population size aged x last birthday in calendar year t. It is assumed that (dx, t) and (ex, t) are each na  ny matrices, thus giving na ages and ny years. We denote the force of mortality (or hazard rate) at exact time t for lives with exact age x by μx, t. The force of mortality can be thought as an instantaneous death rate; specifically, the probability that a life subject to a force of mortality μx, t dies in the interval of time (t, t + dt) is approximately μx, t  dt where dt is small. The force of mortality μx, t for human populations varies slowly in both x and t, and the standard assumption is that μx, t is constant over each year of age, namely, from exact age x to exact age x + 1, and over each calendar year, namely, from exact time t to exact time t + 1. Thus, μxþu,tþv ¼ μx,t for 0  u < 1, 0  v < 1 and therefore μx, t approximates the mid-year force of mortality μx + 0.5, t + 0.5. It is assumed that dx, t is a realisation of a Poisson variable Dx, t:

ð9:1Þ

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 ~ ex,t  μx,t Dx,t Po

ð9:2Þ

The expected values are the product of exposures ex, t and the force of mortality μx, t. Assumption (2) leads to the maximum likelihood estimates of μMLE ¼ mx,t as x,t mx,t ¼

d x,t ex,t

ð9:3Þ

or in a matrix form m ¼ EDc , which means element-wise division in R. Moreover, we also consider the mortality rate qx, t. This is the probability that an individual aged exactly x at exact time t will die between t and t + 1. This enables us to obtain the following relationship between the force of mortality and the mortality rate: 0 qx,t ¼ 1  exp @

Z1

1 μxþs,tþs dsA ¼ 1  eμx,t

ð9:4Þ

0

The following conventions were used for the model: • The αx, βx coefficients will reflect age-related effects. • The κt coefficients will reflect time-related effects. The models were then fitted to historical data. In the Lee and Carter model was proposed the following model for the force of mortality:1 log mx,t ¼ αx þ βx  κ t

ð9:5Þ

it being understood that limitation: na X

βx ¼ 1

ð9:6Þ

κt ¼ 0

ð9:7Þ

x¼1 ny X t¼1

Wherein the second of said restriction means that for each estimate for x will be equal to (at least approximately) to the mean over t of log mx, t. Let ϕ represents the complete set of parameters and the notation for μx, t is extended to μx, t(φ), to indicate its dependence on these parameters. 1

log means natural logarithm.

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For this model, the likelihood function under the Poisson assumption is:
 YY ex,t  μx,t ðφÞ dx,t
 Lðφ; D, EÞ ¼  exp ex,t  μx,t ðφÞ dx,t ! x t

ð9:8Þ

or the log-likelihood lðφ; D, EÞ ¼

XX
   d x,t  log ex,t  μx,t ðφÞ  ex,t  μx,t ðφÞ  log ðdx,t !Þ x

ð9:9Þ

t

and estimation of parameters is by maximum likelihood (MLE). Using Eq. (9.5), the log of the force mortality is expressed as the sum of an age-specific component αx that is independent of time and another component. This second component is the product of a time-varying parameter κ t, which reflects the general level of mortality, and an age-specific component βx, which represents how mortality (rapidly or slowly) at each age varies as the general level of mortality changes. Interpretation of parameters in the Lee-Carter model is not difficult. They should be interpreted as follows: exp(αx) is the general shape of the mortality schedule and the actual forces of mortality change according to the overall mortality index κt modulated by an age response βx. The shape of the βx profile shows which indicators are falling rapidly and slowly over time in response to change in κt. In practice, the fitting of a model is typically only the first step. To forecast time series, we used random walk with drift purpose which is the forecasting of mortality. Additional was assumed that the estimated age parameters, αx, βx, to be invariant over time. This assumption is an approximation. The method has been thoroughly tested, and the results were presented in the work of Booth, Tickle, and Smith (2005) and found to work. It was assumed that the trend observed in past years can be graduated (or smoothed) and that it will continue in future years. Using random walk with drift, the dynamics of κt follows κ t ¼ κt1 þ θ þ εt1

ð9:10Þ


~ 0; σ 2 ). with i.i.d. standard Gaussian distribution εt N ε The value at future time t + h can be written as κ tþh ¼ κt þ h  θ þ

h1 X

εtþs

s¼0

which has Gaussian distribution N(κt þ h  θ; σ 2ε  h).

ð9:11Þ

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Based on the above, it can be assumed that the best point estimate for future value at time t + h is κ t + h  θ and the 95% confidence interval (CI) is 

κt þ h  θ  1:96  σ ε 

pffiffiffi pffiffiffi h; κ t þ h  θ þ 1:96  σ ε  h

ð9:12Þ

where θ is the mean of the first differences Δκt ¼ κ t  κt  1 and σ 2ε is their variance.

9.4

Results

In Table 9.1 (in the Appendix), we plotted the maximum likelihood estimates for the parameters of the Lee-Carter model (L-C model) using the total population data, ages 0–95, for the selected European countries. Model fitting was conducted in R (statistical computing language) and was also used for Fig. 9.4. It is important to note that estimated values for βx were higher at the lowest ages (i.e. for children), which means that at those ages the mortality improvements have been faster in recent decades. Given the progress of kappa_t, it is possible to see a significant change in its values before 1990 and after 1990, as there is a steeper slope for kappa_t after 1990. This is connected to socio-economic changes after the fall of communism. These changes influenced the demographic data, but not at the same intensity. The decreasing trend in κt reflects general improvements in mortality over time for all ages. The κt was then simulated up to 2060 according to Eq. (9.10). This was achieved over 1000 simulations. The results for the total population are plotted in Figs. 9.2 and 9.3 (which illustrate only six simulations for Poland and the Czech Republic). The dashed curves in the plot show the 2.5th and 97.5th percentile of the distribution of κt, resulting in a 95% confidence interval. By forecasting κ t the predictions for the force of mortality μx, t ¼ exp (αx + βx  κ t) were obtained, following which Eq. (9.4) was used to obtain mortality rates qx,t. Table 9.1 (Appendix) Estimated parameters αx, βx,κ t of the L-C model for population of chosen countries. Source: Authors’ processing 40 0 1958 -40

1968

1978

1988

1998

2008

2018

2028

2038

2048

2058

-80 -120 -160

Year

Fig. 9.2 Predicted κ t for total population with 95% for PL. Source: Authors’ processing

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50 0 1950 -50

1970

1990

2010

2030

2050

2070

-100 -150

-200 -250 -300

Fig. 9.3 Predicted κ t for total population with 95% for CzR. Source: Authors’ processing

0.3

0.3

0.25

0.25

2014

0.2

0.2 0.15

0.15

0.1

0.1

0.05

0.05

0

0 65 70 75 80 85 90 95 100

2030

2045

65 70 75 80 85 90 95 100

Fig. 9.4 Observed qx in 2014 and predicted qx in 2030 and 2045 for the total population of Poland and the Hungary. Source: Authors’ processing

To avoid the risk of underestimating the relevant obligations, a dynamic model of mortality was used. The cohort or dynamic life table portrays the future evolution of mortality rates and implies a diagonal arrangement in the projections within the life table (see Table 9.1—example for the Poland). This means that taking the mortality rate for the age of 65 in 2014, this was 66 years in 2015 . . ., 75 years in 2024 . . ., 85 years in 2034, and so on. The results obtained for the Lee-Carter model enabled us to trace the change in probability over time that someone aged exactly x will die before reaching age x + 1 in different years (2014, 2030, 2045) and then present this in graphical form. For example, graphs for Poland and the Hungary are presented in Fig. 9.4. These show that this probability will be lower in subsequent generations. Finally, Eqs. (9.13–9.16) were used to obtain the present values of the annuities, such as term immediate annuity ax:nj and term annuity-due €ax:nj . Annuities payable m-times per year were also considered:

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ax:nj ¼

n X

vt  t p x

ð9:13Þ

t¼1 ðmÞ

ax:nj ¼ ax:nj þ

 m1
 1  vn  n px ðUDDÞ 2m

€ax:nj ¼

n1 X

vt  t p x

ð9:14Þ ð9:15Þ

t¼0 ðmÞ

€ax:nj ¼ €ax:nj 

 m1
 1  vn  n px ðUDDÞ 2m

ð9:16Þ

where (UDD) denotes the assumption of uniform distribution of deaths. For example, we can consider an individual aged 65 in 2015 (birth year ¼ 1950) who wants to purchase a 30-year annuity. To calculate the annuities, we first use the period table, which contains the last available mortality rates. In this case it is year 2014 (the second column of Table 9.2). We then use the diagonal values (cohort table) for the cohort aged 65 in 2015 (born 1950) who are still alive in year 2015 + t. Table 9.2 (in Appendix) provides the present values of 30-year annuities for the individual aged 65 from the whole population at an interest rate of 2% p.a. (or i ¼ 0.02). In this Table, results are presented for all selected countries in two variants. kappa_t is predicted by the random walk with drift. For random walk, we use the whole range of kappa_t data from 1950–2014, except for Poland (1958–2014) and Latvia and Lithuania (1959–2014). These are predicted values with a 95% confidence interval as well as randomly generated values (stochastic). For “Variant 1”, we used the whole range of kappa_t data. For “Variant 2” we used data from 1990–2014. The progress of kappa_t shows that there is a significant change in its values before 1990 and after 1990, as there is steeper slope for kappa_t after 1990. This is connected to socio-economic changes after the fall of communism. These changes influenced the demographic data, but not at the same intensity. There were substantial differences in countries from East Europe but not in those from Western Europe. Table 9.2 (in Appendix). Present values of annuities for the total population in selected European counties (x ¼ 65, n ¼ 30, i ¼ 0.02) Appendix. Source: Authors’ processing This increase varies across the selected countries. In case ax:nj in Variant 1, the lowest value of 1.28% was received for Lithuania and the highest value of 5.34% for France. This increase is not extremely significant if a different constant interest rate was used. In Variant 2 the situation is different. Relative changes between the period and cohort table are higher than in Variant 1. The lowest value was again obtained for Lithuania (1.43%); however, the highest values were for the Czech Republic (8.73%) and for Poland (7.48%). For these two countries, the biggest difference between the values obtained in both variants is also apparent. The spread is

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approximately 3 percentage points. As noted previously, the results in Variant 2 are strongly influenced by the changing trend in kappa_t after 1990. Therefore, developing countries after 1990 are most strongly affected by demographic changes. This effect is no longer as strong as it is in developed countries (France and Sweden). Moreover, it is not yet visible in countries whose level of development in 1990 was low (such as Lithuania and Latvia).

9.5

Conclusions

Every year, national governments and WHO announce life expectancy. However, it should be emphasised that life expectancy is not an appropriate measure of risk for financial institutions. The expected length of a vein does not indicate how mortality rates at different ages change over time. However, longevity risk indicators cannot be too complicated. The indicator of a huge number of numbers is difficult to interpret and will lose its purpose as a “summary” of mortality patterns needed for modelling. Stochastic models were therefore used to analyse mortality and an explanation given as to how they may be fitted. This made it possible to turn to the industry requirement to forecast future mortality. After calculation, it was shown that, if the current rate of increase continues, the cohort life table should be used instead of the period life table to increase the present value of pension liabilities in defined-benefit schemes at age 65 (provided everything else remains unchanged). As mentioned previously, period tables are static tables built on the basis of mortality behaviour observed in a population during one period, while cohort tables incorporate projections of future trend in mortality that take account of observed changes over time, at birth, and at different ages for different generations. The different demographic institutes across countries do not construct cohort tables as frequently as they do period tables. In fact, for most countries, information on observed and projected life expectancy is based on static calculations (often jointly collected by international organisations such as the UN, the World Bank, Eurostat, and OECD) and is systematically used in calculations related to pensions, health, long-term care, and welfare status. By contrast, it is rare to find life expectancy estimates based on cohort tables. As was shown when calculating the term pension for selected countries, if the initial capital (e.g. retirement capital) is charged on period tables and not on cohort tables, it will be too low to cover the retirement plan. However, the forecasting of mortality should be approached with both caution and humility. Any prediction made is unlikely to be correct. It is also important to be aware of the model risk when assessing longevity-related liabilities, especially for annuities and pensions. The fact that parameters can be estimated does not imply they can be forecast sensibly. The Lee-Carter model also has many critics. Some of these have argued that many age-specific rates are so low they cannot realistically be

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projected to decline much further. Others have questioned whether αx, βx should be treated as invariant. Another limitation inherent in the Lee-Carter model is that a long data series is required for fitting. This means it may be invalid for many developed countries. Such forecasting should enable actuaries to examine the financial consequences using different models and hence to come to an informed assessment of the impact of longevity risk on the portfolios in their care. Longevity expectations continue to increase across the developed world. As this occurs, defined-benefit pension funds, a primary holder of this risk, have to recognise this in their actuarial valuations. This increases their liabilities and puts their finances under further pressure.

Appendix

Table 9.1 Period life table vs. cohort life table (for the total population of Poland) qx,t . 65 66 67 68 69 70 71 .

2014 . 0.01696 0.018123 0.019067 0.020073 0.021179 0.023024 0.024367 .

2015 . 0.016816 0.017961 0.018878 0.019856 0.020929 0.022748 0.024054 .

Source: Authors’ processing

2016 . 0.016674 0.017801 0.018692 0.019641 0.020682 0.022476 0.023744 .

2017 . 0.016532 0.017642 0.018507 0.019429 0.020437 0.022206 0.023438 .

2018 . 0.016392 0.017484 0.018323 0.019219 0.020196 0.02194 0.023137 .

2019 . 0.016253 0.017328 0.018142 0.019011 0.019957 0.021677 0.022839 .

2020 . 0.016116 0.017173 0.017962 0.018805 0.019721 0.021417 0.022545 .

Lithuania

Hungary

France

The Czech Republic

Period table Cohort table Relative change

97.50%

Period table Cohort table Relative change 2.50%

97.50%

Period table Cohort table Relative change 2.50%

97.50%

Period table Cohort table Relative change 2.50%

Variant 1 ax:nj 14.04 14.75 5.01% 14.13 0.65% 15.34 9.26% 16.24 17.11 5.34% 16.45 1.29% 17.72 9.08% 12.99 13.53 4.16% 13.04 0.42% 14.01 7.91% 12.98 13.14 1.28% 14.31 15.01 4.85% 14.4 0.61% 15.60 8.99% 16.50 17.35 5.19% 16.70 1.24% 17.96 8.85% 13.25 13.79 4.00% 13.3 0.38% 14.27 7.64% 13.25 13.43 1.36%

ð12Þ ax:nj

14.74 15.43 4.69% 14.83 0.60% 16.01 8.64% 16.90 17.72 4.89% 17.09 1.16% 18.30 8.31% 13.69 14.22 3.88% 13.74 0.39% 14.7 7.36% 13.68 13.83 1.14%

ð12Þ € ax:nj

€ ax:nj 15.01 15.69 4.54% 15.09 0.57% 16.27 8.39% 17.15 17.97 4.75% 17.34 1.11% 18.54 8.10% 13.96 14.48 3.74% 14.01 0.35% 14.95 7.11% 13.95 14.12 1.22% Period table Cohort table Relative change

97.50%

Period table Cohort table Relative change 2.50%

97.50%

Period table Cohort table Relative change 2.50%

97.50%

Period table Cohort table Relative change 2.50%

Variant 2 ax:nj 14.04 15.27 8.73% 14.8 5.41% 15.72 11.96% 16.24 17.24 6.12% 16.67 2.61% 17.77 9.38% 12.99 13.79 6.19% 13.34 2.72% 14.24 9.64% 12.98 13.16 1.43%

ð12Þ

ax:nj 14.31 15.52 8.46% 15.06 5.22% 15.97 11.60% 16.50 17.48 5.95% 16.91 2.52% 18.00 9.14% 13.25 14.04 5.96% 13.6 2.60% 14.49 9.31% 13.25 13.45 1.52%

Table 9.2 Present values of annuities for the total population in chosen European counties (x ¼ 65, n ¼ 30, i ¼ 0.02) Appendix ð12Þ

€ ax:nj 14.74 15.93 8.13% 15.48 5.04% 16.37 11.12% 16.90 17.84 5.60% 17.3 2.37% 18.35 8.58% 13.69 14.48 5.76% 14.04 2.54% 14.91 8.95% 13.68 13.85 1.27%

(continued)

€ax:nj 15.01 16.19 7.88% 15.74 4.87% 16.63 10.79% 17.15 18.09 5.44% 17.55 2.29% 18.58 8.36% 13.96 14.73 5.55% 14.3 2.43% 15.16 8.64% 13.95 14.14 1.36%

9 Modelling the Life Expectancy of Elderly People for Life Insurance and Pension. . . 139

Sweden

Poland

Latvia

Table 9.2 (continued)

Period table Cohort table Relative change 2.50%

97.50%

Period table Cohort table Relative change 2.50%

97.50%

Period table Cohort table Relative change 2.50%

97.50%

2.50%

Variant 1

13.14 0.8% 13.7 3.45% 13.05 13.32 2.07% 12.78 2.1% 13.85 6.09% 14.34 15.00 4.61% 14.49 1.08% 15.49 8.06% 15.83 16.5 4.27% 16.03 1.27%

ax:nj 12.87 0.8% 13.4 3.29% 12.78 13.05 2.13% 12.5 2.2% 13.58 6.26% 14.07 14.74 4.75% 14.23 1.13% 15.24 8.29% 15.56 16.24 4.38% 15.77 1.32%

ð12Þ ax:nj

0.8% 14.08 2.95% 13.48 13.75 1.99% 13.21 2.1% 14.27 5.82% 14.76 15.41 4.41% 14.92 1.04% 15.9 7.69% 16.24 16.9 4.07% 16.43 1.21%

13.57

ð12Þ € ax:nj

0.7% 14.38 3.11% 13.76 14.02 1.93% 13.48 2.0% 14.54 5.67% 15.03 15.67 4.28% 15.18 0.99% 16.15 7.48% 16.5 17.15 3.96% 16.69 1.17%

€ ax:nj 13.85

Period table Cohort table Relative change 2.50%

97.50%

Period table Cohort table Relative change 2.50%

97.50%

Period table Cohort table Relative change 2.50%

97.50%

2.50%

Variant 2

0.91% 13.44 3.62% 12.78 13.3 4.06% 12.65 1.01% 13.94 9.08% 14.07 15.13 7.48% 14.7 4.47% 15.56 10.55% 15.56 16.43 5.60% 16.03 3.01%

ax:nj 12.86 0.89% 13.75 3.80% 13.05 13.57 3.94% 12.92 0.99% 14.21 8.84% 14.34 15.38 7.26% 14.96 4.32% 15.81 10.25% 15.83 16.69 5.45% 16.29 2.92%

ð12Þ

ax:nj 13.13 0.86% 14.12 3.24% 13.48 13.99 3.77% 13.36 0.95% 14.62 8.44% 14.76 15.79 6.93% 15.37 4.14% 16.2 9.75% 16.24 17.08 5.19% 16.69 2.78%

ð12Þ

€ ax:nj 13.56

0.84% 14.43 3.43% 13.76 14.26 3.67% 13.63 0.94% 14.89 8.22% 15.03 16.04 6.72% 15.63 4.00% 16.45 9.48% 16.5 17.33 5.05% 16.95 2.70%

€ax:nj 13.83

140 A. Jędrzychowska and J. Gogola

97.50%

Period table Cohort table Relative change 2.50%

Source: Authors’ processing

Slovakia

97.50%

16.95 7.14% 13.56 14.12 4.09% 13.64 0.53% 14.6 7.64%

16.7 7.32% 13.29 13.86 4.24% 13.37 0.57% 14.34 7.91%

17.34 6.79% 13.99 14.55 3.97% 14.07 0.54% 15.03 7.38%

17.59 6.62% 14.26 14.81 3.83% 14.33 0.49% 15.28 7.13% 97.50%

Period table Cohort table Relative change 2.50%

97.50%

16.82 8.09% 13.29 14.1 6.08% 13.68 2.88% 14.52 9.25%

17.07 7.88% 13.56 14.36 5.85% 13.94 2.75% 14.78 8.93%

17.45 7.49% 13.99 14.79 5.67% 14.37 2.70% 15.2 8.61%

17.71 7.30% 14.26 15.04 5.47% 14.63 2.58% 15.45 8.32%

9 Modelling the Life Expectancy of Elderly People for Life Insurance and Pension. . . 141

Fig. 9.5 Estimated parameters αx, βx, κ t of the L-C model for population of chosen countries. Source: Authors’ processing

142 A. Jędrzychowska and J. Gogola

143

Fig. 9.5 (continued)

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References Allison, D. S., & Ludwig, D. S. (2005). A possible decline in life expectancy in the United States in the 21st century. New England Journal of Medicine, 352, 1103–1110. Antolin, P. (2007). Longevity risk and private pensions. OECD working papers on insurance and private pensions, no. 3. Paris: OECD Publishing. Ayuso, M., Bravo, J., & Holzmann, R. (2017). On the heterogeneity in longevity among socioeconomic groups: Scope, trends, and implications for earnings-related pension schemes. Global Journal of Human Social Sciences-Economics, 17(1), 33–58. First published as BBVA Working Paper 16. Blake, D., El Karoui, N., Loisel, S., & MacMin, R. (2017). Longevity risk and capital markets. Special Issue Insurance: Mathematics and Economics, 63. Blaschke, E. (1923). Sulle tavole di mortalita variabili col tempo. Giornale di Mathematica Finanziara, 5, 1–31. Booth, H., & Tickle, L. (2008). Mortality modelling and forecasting: a review of methods. Annals of Actuarial Science, 3(1–2), 3–43. Booth, H., Tickle, L., & Smith, L. (2005). Evaluation of the variants of the Lee-Carter method of forecasting mortality. A multi-country comparison. New Zealand Population Review, 31(1), 13–34. Bravo, J. (2007). Period and prospective life tables. Stochastic models, actuarial applications and longevity risk hedging. PhD Thesis in Economics, University of Évora. Gogola, J. (2014a). Lee-carter family of stochastic mortality models. In Managing and modelling of financial risks. Proceedings of the 7th International Scientific Conference (pp. 209–2017). Ostrava: VŠB-TU Ostrava. Gogola, J. (2014b). Stochastic mortality models. Application to CR mortality data. In Mathematical and statistical methods for actuarial sciences and finance (pp. 113–116). Cham: Springer. Gogola, J. (2014c). A comparison of Lee-Carter and Cairns-Blake-Dowd stochastic mortality models. In Proceedings of the 16th international conference on mathematical methods, computational techniques and intelligent systems (MAMECTIS 14) (pp. 130–134). Lisbon: WSEAS Press. isbn:978-960-474-396-4. Gogola, J. (2015). Comparison of selected stochastic mortality models. International Journal of Mathematical Models and Methods in Applied Sciences, 9, 159–165. Gogola, J., & Slavíček, O. (2016). Pension-related application of the cohort life table. In European financial systems 2016, proceedings of the 13th international scientific conference (pp. 191–198). Brno: Masaryk University. isbn:978-80-210-8308-0. Human Mortality Database. (1997). University of California, Berkeley (USA) and Max Planck Institute for Demographic Research (Germany). www.mortality.org Jindrová, P., & Slavíček, O. (2012). Life expectancy development and prediction for selected European countries. In Managing and modelling of financial risk. Proceedings of the 6th international scientific conference (pp. 303–312). Ostrava: VŠB-TU. Kannisto, V. (2000). Measuring the compression of mortality. Demographic Research, 3(6), 1–24. Oeppen, J., & Vaupel, J. W. (2002). Enhanced: broken limits to life expectancy. Science, 296, 1029–1031. Olshansky, S. J., Carnes, B. A., Hershow, R., Passaro, D., Layden, J., Brody, J., Hayflick, L., Butler, R. N., Allison, D. B., & Ludwig, D. S. (2005). Misdirection on the road to Shangri-La. Science of Aging Knowledge Environmen, 2005(28), pe15. http://sageke.sciencemag.org/cgi/ content/full/2005/22/pe15. Pacáková, V., & Jindrová, P. (2014). Quantification of selected factors of longevity. In Proceedings of the 2014 international conference on mathematical methods in applied sciences (MMAS’14) (pp. 170–174). Saint Petersburg: State Polytechnic University. Pacáková, V., Jindrová, P., & Seinerová, K. (2013). Mortality models of insured population in the Slovak Republic. Modelling and forecasting of socio-economic phenomena. In Proceedings of

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the 7th professor Aleksander Zelias international conference, 7–10.09.2013, Zakopane (pp. 99–106) Robine, J. M., & Vaupel, J. W. (2002). Emergence of super centenarians in low mortality countries. North American Actuarial Journal, 6(3), 54. Siegel, J. (2005). The great debate on the outlook for human longevity: exposition and evaluation of two diverging views., Society of Actuaries The Economist. (2010). Longevity swaps: live long and prosper. London: Economist Group. Wilmoth, J. R., & Horiuchi, S. (1998). Deceleration in the age pattern of mortality at older ages. Demography, 35(4), 391–412. Wilmoth, J. R., & Horiuchi, S. (1999). Rectangularization revised: variability of age at death within human populations. Demography, 36(4), 475–495.

Chapter 10

The Challenges for Life Insurance Underwriting Caused by Changes in Demography and Digitalisation Ilona Kwiecień, Patrycja Kowalczyk-Rólczyńska, and Michał Popielas

10.1

Introduction: The Main Concepts Involved in Underwriting Life Insurance

The insurance market specialises in risk trading. Insurance companies collect information on risks in various ways and accept them at an appropriate price (premiums). Insurance risk assessment is an underwriting function and is one of the key business processes in an insurance company’s operational activities. As a mechanism for financial stability, underwriting contributes to the formation of a well-diversified and profitable insurance portfolio for the insurer (Mkrtychev and Enik 2018). Underwriting is a concept that derives from English and refers to the process of selecting and classifying insurance applications (Vaughan 1982) and evaluating and pricing the risks proposed for the insurance. This involves the insurer deciding whether a particular risk is acceptable and, if so, whether the normal terms and conditions will apply. The underwriter monitors the classes of businesses that are underwritten and regularly reviews rates and strategies (Benett 2004). The main purposes of underwriting areas follow (Poprawska and Jędrzychowska 2016): • Prevention of risk anti-selection (negative selection): anti-selection occurs when the demand for insurance is reported by people who are considered to be an above-average risk—ensuring the balance of the portfolio (more damaging risk categories should be compensated for by less damaging categories) and proper selection of the number of individual risk categories so that their total loss ratio is not higher than assumed. Thus, the future loss ratio for a given insurance group is close to the historical course on which the tariff premium was determined. I. Kwiecień (*) · P. Kowalczyk-Rólczyńska · M. Popielas Wroclaw University of Economics and Business, Wroclaw, Poland e-mail: [email protected]; [email protected]; michal.popielas@ue. wroc.pl © Springer Nature Switzerland AG 2020 M. Borda et al. (eds.), Life Insurance in Europe, Financial and Monetary Policy Studies 50, https://doi.org/10.1007/978-3-030-49655-5_10

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• Ensuring the profitability of insurance operations: the aim of underwriting is to produce and maintain a profitable account of customers in a constantly changing business environment (Atkins and Bates 2008). Thus, the goal of underwriting is not to select risks that incur no losses: it is to avoid a disproportionate number of bad risks, thereby equalising the actual losses with those expected. In addition to this goal, underwriting has several other objectives. While attempting to avoid adverse selection through the rejection of undesirable risks, the underwriter must secure an adequate volume of exposures in each class. Additionally, they must guard against the congestion or concentration of exposures that might result in a catastrophe (Vaughan and Vaughan 2008). Information asymmetry in insurance creates the need for underwriting. The problem of information asymmetry is that one of the parties to the contract (the client) has more information about the individual risk than the insurance company. Improvements in underwriting techniques are principally required to prevent: • Incorrect risk classification and moral hazard (the temptation to conceal information by insurance candidates), which may lead to the acceptance of unwanted risks or incorrect price calculations. • Sale (mis-selling) or purchase of a product not adapted to needs (mis-buying). This generates a risk to reputation, supervision sanctions, liability, and either mis-selling cases on the part of the insurer or a lack of real protective insurance and externalisation of losses on the part of the buyer. The organisation of the underwriting process is therefore vital for both parties to the contract. The basic purpose of underwriting in life insurance is to identify risk factors and forecast their impact and the effects of treatment (or its omission) on the length of life or the probability of death. With respect to the risk covered characteristic, which is life, life insurance underwriters take into consideration several factors, including the medical, lifestyle, and personal characteristics of an individual proposed for life cover, such as their age, to ascertain their risk profile (Mutai et al. 2017). Collective population data also affect the risk assessment process and risk valuation, which means that demographic risk is an important and necessary element of the underwriting process. The specificity of underwriting in life insurance is related to the specificity of the risk covered through long-term protection and regulations, which often limits the potential for modifying the terms of the protection, including post-contract premiums. This necessitates progressive analysis and the prediction of changes in individual behaviour or demographic trends. The evolution of underwriting processes, as well as the challenges and opportunities posed by demography and new technologies, will be discussed in later sections.

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10.2

From Health Questionnaires to Genetic and “Pay as you Live” CONCEPTs: The Development of Life Insurance Underwriting

Changes over the years in the life insurance underwriting process constitute a history of the development of techniques, conclusions based on the experience of actuaries and insurance companies, and the implications of changes in the areas of risk covered by insurance. Guzel–Szczypiórkowska and Visan (1996) divided this developmental process into several stages. In the first stage, comprising the development of risk assessment systems that lasted until the end of the nineteenth century, preliminary health questionnaires concerning the applicant and their family were commonly used, along with a description of their work. These had to be supported with references from two people known to the insurance company. Circa 1870, medical examinations were introduced into the American insurance market. These permitted a more accurate selection of the insured risk. Initially, however, they were only used for certain types of medical risk. The second period (from the beginning of the twentieth century) was a medical and statistical stage where increasing attention was paid to the dependence of individual risk factors on the mortality of those insured. Studies on the impact of weight showed that the risk of mortality increased when people were overweight. The dependencies that were obtained initiated mass medical and actuarial research. During the period from 1885 to 1908, approximately 750 thousand men and 400 thousand women were tested in the USA. Based on the results of this research, lists of 100 risky professions were established and the applicants then divided into risk classes depending on their health status. ECGs, X-rays, and blood analyses were then used in the insurance process. In 1919, O. Rogers and A. Hunter of the New York Life Insurance Company developed a new method of risk assessment that constituted a numerical method for determining insurance risk. This was based on a comparison of the probability of death in the year analysed between the weighted averages of two groups. The counter was the observed group and the denominator was the appropriate control group. This was a revolution in risk estimation in life insurance as it meant that the risk of death for diseases not yet covered (substandard risks) could now be assessed. In the third period—the case-actuarial stage—the numerical insurance risk assessment system underwent significant development. There was an increasing awareness of the need to use prospective studies and current disease statistics from hospitals and clinics in place of historical data. People suffering from diseases rated as high risk (and thus not insured) could now be insured in the light of current medical knowledge, and their illnesses could be accurately diagnosed and estimated for the purposes of life insurance. Furthermore, strong competition in the life insurance market meant that, after conducting thorough medical tests, a large number of people with weaker health could now be insured. Circa 1980, differentiation in underwriting smokers/non-smokers began in the US market, which was perceived as the beginning of preferred underwriting (Klein

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2013). This developed at time when the danger of contracting HIV was monitored through blood tests. Preferred underwriting recognises that each individual’s health and lifestyle is unique. An applicant’s class of health is thus determined by assessing specific factors to provide a more accurate prediction of life expectancy. The factors analysed in the “preferred” approach have already become a classic canon and include the following (IAA 2014; Klein 2013): • • • • • • • • •

Alcohol and drug abuse Blood pressure Build Cholesterol Family history MVR Personal medical history Tobacco use Others—aviation, avocations, citizenship, foreign travel, hazardous activities, and residence

In the 1980s, the possibility of using genetic tests in preferred underwriting emerged. This facilitated the identification of at-risk individuals who were asymptomatic or who will never become significantly impaired. Research subsequently found that genetic discrimination existed and was manifested in numerous social institutions, especially in the health and life insurance industries (Billings et al. 1992). This incited discussions among medical, scientific, legal, and social policy experts regarding possibilities, boundaries, ethics, and discrimination risks (Joly et al. 2003; Mould 2003). Modern reports indicate that diagnostic genetic tests, which confirm a disease in a symptomatic individual insofar as it already exists in medical records, are routinely assessed in life insurance underwriting. However, regulations regarding the possibilities and methods for using these tests differ. Such differences are based not only on whether they are diagnostic or predictive tests but also on the extent to which the family history is considered to be genetic information (IAA 2014; Nabholz and Rechfeld 2016). Thus, countries either do not allow genetic tests (as in Poland or Portugal), do not allow them unless the applicant proposes their use (Italy), or allow them only above certain assured sums (e.g. Germany—when the sum assured exceeds EUR 300,000, and the UK, where the sum assured for a predictive test is up to GBP 500,000). Detailed information on the regulations for each country can be found in reports by the (International Actuaries Association (IAA) 2014; Nabholz and Rechfeld 2016). To counteract discrimination, gender discrimination has also been banned. For instance, regulation (EU Anti-discrimination) on equal treatment between men and women in pricing, premiums, and underwriting was introduced in the EU. Unisex underwriting standards became sanctioned in the EU from 21 December 2012, according to the Test-Achats case (Court of Justice of the European Union, Judgment of the Court (Grand Chamber) of 1 March 2011. Case No. C-236/09). Companies that fall under the EU regulations are now re-examining their requirements to meet the standard while providing protective value.

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In recent years, the increasingly popular use of mobile applications in motor insurance (pay as you drive) initiated the process of using data from electronic devices in life insurance (pay as you live). The progress of digitisation and the availability of new electronic tools has now provided the insurer with new opportunities to obtain data on insurance candidates that can be used in risk assessment. Applications can be used to collect and transfer data quickly, cheaply, and efficiently (Błajda et al. 2017). Digital transformation grants insurance companies exceptional capabilities, but it also creates enormous expectations. The digital age has created data ecosystems that have the capability to completely supplant traditional life insurance underwriting models. In the past, the only way to underwrite an individual was to subject them to a tedious array of paperwork and health examinations. By coupling electronic health records with social and public third-party data, insurers can provide real-time pricing based on ever more accurate risk ratings (Spano 2019). In contemporary actuarial analyses, possible approaches and directions for development appear to be focused around demographic and behavioural changes (old age underwriting, living “online”, the need for speed”) and the need for the expansion of preferred underwriting, mostly through the power of new technologies such as teleinterviewing, tele-underwriting, and electronic underwriting systems as well as new sources of information such as national databases or financial institutions (tax records, bank credits, transactions with products, and service purchases structure) or social media (IAA 2014; Klein 2013). Given these challenges, the next section will focus further on demographics and new technologies.

10.3

Challenges Due to Demographic Trends

Recent years have witnessed several significant demographic changes, especially in relation to life expectancy, which has been increasing in most developed European countries since 1960. Based on European Commission data (2017) for both males and females, life expectancy at birth across the EU increased on average by approximately 11 years between 1960 and 2016: from 66. 9 years to 78.3 years for males and from 72.3 years to 83.7 years for females. This increase in life expectancy is also expected to continue (see Table 10.1). Alongside the positive and systematic extension of average life expectancy, other demographic conditions have emerged that are driving changes in the age structure of the population. Most important of these is a low fertility rate. European Table 10.1 The average life expectancy at birth for EU countries

Males Females

1960 66.9 72.3

1980 68.9 75.8

2000 72.7 79.6

2016 78.3 83.7

2060 84.9 89.2

Source: Own study based on European Commission, The 2018 Aging Report, Underlying Assumptions and Projection Methodologies, pp. 15–16

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Table 10.2 Total fertility rate for EU countries Total fertility rate

1960 2.67

1980 1.97

2000 1.48

2016 1.58

2030 1.69

2060 1.78

2070 1.81

Source: Own study based on European Commission, The 2018 Aging Report, Underlying Assumptions and Projection Methodologies, pp. 15–16 Table 10.3 Average old-age-dependency ratio and very old-age dependency ratio for EU countries Old-age-dependency ratio (65+/(15–64))a Very old-age dependency ratio: (80+/(15–64)b

1960 15.2 2.3

2000 23.4 5.0

2016 29.6 8.3

2060 51.6 21.6

2070 51.2 22.3

Source: European Commission, The 2018 Aging Report, Economic and Budgetary Projections for the 28 EU Member States (2016–2070) a This indicator is the ratio between the number of persons aged 65 and over (age when they are generally economically inactive) and the number of persons aged between 15 and 64. The value is expressed per 100 persons of working age (15–64) b This indicator is the ratio between the number of persons aged 80 and over and the number of persons aged between 15 and 64. The value is expressed per 100 persons of working age (15–64) Table 10.4 Projected life expectancy at 65 for EU countries

Males Females

2016 18.1 21.5

2060 22.6 25.8

2070 23.4 26.6

Source: Own study based on European Commission, The 2018 Aging Report, Underlying Assumptions and Projection Methodologies, pp. 15–16

Commission data show that, since the 1980s, fertility levels in Europe have not been sufficient to guarantee generational replacement. Moreover, fertility rate forecasts do not suggest that generational replacement will occur in the following years (see Table 10.2). The above phenomena significantly affect the intensity of the ageing process within the population, which is also confirmed by the growing demographic burden coefficients for the elderly (see Table 10.3). Forecasts presented by the European Commission indicate that, by 2070, the rate at which the population in Europe is ageing will increase significantly. Particularly important are the relatively high values of the very old-age dependency ratio, as this will determine increases in the demand for and expenditure on health care and long-term care for older people in the second and third stages of old age.1 The need to pay more attention to the elderly is also confirmed by life expectancy forecasts for people aged 65+ (see Table 10.4). In the EU, life expectancy at 65 for males is expected to increase by 5.3 years over the projected period, from 18.1 in

The World Health Organization distinguishes three stages of old age: “young old” (60–74 years), “old old” (75–84 years), and “oldest old” (85 years and older). More: World Health Organization, A glossary of terms for community health care and services for older persons, 2004

1

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85 and over 80-84 75-79

generation silver

70-74 65-69 60-64 55-59 50-54

45-49 40-44 35-39 30-34 25-29 20-24 15-19 10-14

generation Z

5-9 0-4 30000000

20000000

10000000

10000000 Males

20000000

30000000

Females

Fig. 10.1 Population by age and gender in 2018 (EU—28 countries). Source: Own study based on data from https://ec.europa.eu/eurostat

2016 to 23.4 in 2070. For females in the EU, life expectancy at 65 as a whole is projected to rise by 5.1 years, from 21.5 in 2016 to 26.6 in 2070. The continuing low levels of fertility and a simultaneous increase in the length of human life have led (and will continue to lead) not only to a gradual decrease in the population but also to changes in the structure of the age pyramid. This will consist in a change in the relationship between the numbers of the youngest generation (generation Z), working-age population/working people, and the oldest population (generation silver). Furthermore, in the population age pyramid for 2018 (see Fig. 10.1), the number of women belonging to the silver generation is noticeably higher than the number of men. The feminisation of the elderly group has primarily been caused by the excessive mortality of men and the divergence in life expectancy parameters— women reaching the age of 65 have a life expectancy 3.4 years higher than men (see Table 10.4). This analysis of demographic data shows that older people present a significant challenge for life insurers. For instance, longer life expectancy allows people to extend the acceptance insurance period and also products for a longer time. Market analysis shows the extension of the acceptance period in life insurance policies

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renewed up to 95 years of age (State Farm, based on: Best Life Insurance for Seniors: Our Top 5 Options, https://www.doughroller.net/insurance/best-life-insurance-forseniors). This is also related to extensions of mortgages and short-term loans offered by banks for the elderly. However, the growing proportion of the 60+ generation in the pyramid—while raising the age at which young people in developed countries decide to have a child and therefore the moment at which this becomes an important factor influencing decisions to buy life insurance (Spano 2019)—means that the old-age generation is gaining importance as a group of potential life insurance buyers. This confirms the challenge identified in market research in the area of old-age underwriting. It is clear that this will require matching risk assessment standards; not just through the classic consideration of age as a factor, it will also be achieved by identifying the unique features of the older generation. Reports by the IAA (2014) have highlighted the need to change the traditional levels of underwriting acceptance; lower readings on blood pressure, cholesterol, and weight, for example, could be indicative of more serious problems than higher readings for the elderly and, in addition, raise the possibility of using: • Cognitive testing—tests for dementia and other cognitive impairments. • Functional testing—tests for frailty. • Supplementary questionnaires—this may include questions on social, mental and physical activities, daily living activities, living arrangements, and travel. However, it should be emphasised that research on generations indicates that older people are not a homogeneous group. A group born before 1945 (sages, silent) and from 1945 to 1964 (baby boomers) are distinct from later divisions. In 2019, this means a limit of
74 determines generational differences in life attitudes, physical and social activity, and medical data (Brown et al. 2015; Dimock 2019). These differences mean it is important to identify and take account of these in the underwriting process.

10.4

Challenges Due to New Technologies

Digitisation, including the use of mobile applications, is a process that has been ongoing for several years in all sectors of the economy: domestic, EU, and global. The pace, level of development, and priorities of digital transformation vary from one country to another depending on industries and business entities (Rekosz et al. 2018). Globally, organisations are acutely aware of the development of technology and the resulting changes in consumer behaviour and preferences, for which digital solutions have become an essential element of life. The aforesaid processes also have an impact on the insurance sector. Digitisation itself also affects internal processes in insurance companies such as underwriting. The potential associated with the digitisation of the insurance sector is significant.

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The bar for service quality and channel availability has been rising dramatically, driven by innovative digital players in the B2C arena. European customers, such as those in other mature and emerging markets, are pressurising life insurers to deliver customer experience at a higher level—requiring changes in management, investments, and transformation (Binder et al. 2018). Insurers feel under pressure to reinvent their businesses to keep pace with rapidly evolving technologies, competition, regulations, types of risk, and consumer expectations. Approximately 82% of the 623 insurance executives who responded to the Tech Vision 2018 survey concurred that their organisations must innovate at an increasingly rapid pace, just to maintain a competitive edge (Costonis et al. 2018). The ability to process ever-increasing volumes of customer data from search engines and shopping portals, data on social media activity, and data from devices and mobile applications provides insurers with high-quality data that enable them to provide personalised insurance purchase offers to customers. The world’s largest Internet companies, most notably Google, Apple, Facebook, and Amazon (“GAFA”), are also developing projects related to the financial sector, exploiting their control over large datasets. These companies have a wealth of information about individual clients that can potentially be employed to assess risk or design insurance products (Łańcucki 2018). The use of artificial intelligence and automation is also becoming increasingly vital. Characterised by increasing autonomy and sophisticated capabilities, intelligent automation in insurance is evolving from robotic automation to AI and moving from the back-office to the front-line of customer services. In so doing, it is enabling insurers to supercharge their efficiency and elevate the customer experience. Surveys have shown that four out of five insurance executives believe that, within the next 2 years, artificial intelligence will work alongside humans in organisations as co-workers, collaborators, and trusted advisors (Costonis et al. 2018). Insurtechs approach insurance in a new way, pioneering the use of new technology and increasing the focus on customers. Advances in digital tools and services are occurring at a time when the life insurance industry is encountering fundamental challenges (McKinsey 2016). Insurtechs should therefore serve as a catalyst for improvement and innovation within the mainstream industry (Costonis et al. 2018). Companies that sell electronic devices, smartphones, smartwatches, and smartbands compete in making functions available in mobile applications that register a lifestyle. For instance, mobile applications in smartwatches and smartbands can, inter alia, measure heart rate at the wrist, analyse sleep, count the number of steps walked, measure the number of calories burnt, and analyse physical activity and sports with the possibility to choose a discipline.2 Applications in smartwatches can measure cardio-respiratory fitness and aerobic performance capacity. For instance, the analytics engine embedded in smartwatches reliably estimates performance capacity by identifying, analysing, and interpreting meaningful

2

More information can be found on: https://buy.garmin.com/pl-PL/PL/p/603201.

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Fig. 10.2 The “pay as you live” concept. Source: Own elaboration based on Ernst and Young, PAYL Insurance (2015)

performance data during a run.3 Information on current physical activity, sports, life parameters (heart rate, respiratory rate), sleep patterns, and lifestyle can be a valuable source of information when the insurer makes decisions as to whether the candidate is accepted for life insurance. This information can also be useful for monitoring lifestyle and health status during the insurance coverage period. The development of new technologies and the use of mobile applications has also led to the creation of a new insurance sales model called “pay as you live” (Ernst and Young 2015). This is a relatively new insurance concept (see Fig. 10.2) aimed at improving controllable behaviours to reduce premiums and reward the insured person. It involves clients providing ongoing data to the insurance company about their lifestyles through existing and new data sources (e.g. wearable technology) as well as the insurer encouraging customers to live a healthier lifestyle to reduce the risk of death and chronic or critical illness. The user can also track their own habits through an easily accessible mobile interface, using the data to monitor and alter behaviours such as diet and exercise to improve health outcomes. Insurers can use the behavioural data generated to personalise solutions based on an enhanced understanding of a customer’s health status and relevant risk factors. This consequently provides the policy holder with rewards and discounts. By using the

3 More information can be found on: https://www.garmin.com/en-IE/performance-data/running/ #physiological-measurements.

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wrist-based heart rate technology in wearables, it is possible to monitor key aspects of fitness and wellness to show how the body responds under various circumstances. It also tracks heart rate variability (HRV), which is employed to calculate and track stress levels, while advanced sleep monitoring provides a greater understanding of how much time the person is spending in light, deep, and REM stages of sleep. Insurers try to create products based on applications that proactively encourage and reward healthier living. Health and fitness applications are now the fastest growing category on iTunes and Android (Ernst and Young 2015). Similar models are used in motor insurance, where the “pay as you drive” or “pay how you drive” model has become popular on the motor insurance market. In this model, the insurance premium is calculated on the basis of data from mobile applications and is based on driving fluidity, overcoming distances, behaviour, and adherence to speed limits and road signs.4 The implementation of solutions based on modern technologies is connected with the ability of insurers to gain access to new data sources. Overall, 60% of insurers recognise the benefits of using data collected by IoT (Internet of Things) devices, such as wearables, telematic devices installed in vehicles, or intelligent sensors in apartments (Rekosz et al. 2018). Additional information can be employed by insurance companies to improve their underwriting and pricing models, as well as generate savings. Across industries, the next generation of intelligent solutions is now moving into physical environments, and key insurer strategies are based on pushing intelligence into—and gathering data from—the physical world via the Internet of Things. The next step will involve automating actions at the edge, potentially helping people to avoid making any insurance claims (Rekosz et al. 2018). Researchers have also foreseen the impact of digitalisation on the insurer’s value chain in the area of underwriting, anticipating its potential relevance in (Eling and Lehmann 2018): • Using artificial intelligence for risk assessment • Using big data and blockchain to analyse more information from numerous sources (mostly automatically) • Using the IoT to monitor the insured risk and tailor the prices accordingly • Digitising the process and storing the contract information

4 More information can be found on: https://www.amodo.eu/porsche-releases-pay-how-you-drivephyd-cash-back-concept/.

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Attitudes Towards Monitoring Health and Lifestyles by Insurers

Insurers encounter barriers when they implement digital initiatives. These are often of a technological nature, such as the lack of advanced solutions or non-adaptation to the specifics of the insurance business. However, significant barriers also arise as a result of legal regulations or a lack of decisions regarding the possibility of using a given technology. The necessity to change consumer habits is often a hindering factor. Few studies have examined social attitudes in terms of the acceptance of data sharing and consent to the use of modern technologies to monitor health or lifestyles in the field of life insurance. However, numerous reports indicate that the progress of digitisation seems to be unavoidable and is inseparable from the further development of underwriting processes. This is demonstrated by the growing interest in electronic equipment and mobile applications. The popularity and availability of smartphones is growing every year. Based on surveys of 26,000 consumers in 26 countries, Sovie et al. (2017) identified the percentage of consumers who planned to buy a smartphone in 2014–2017. This percentage increased from 52% in 2014 to 54% in 2017. Surveys conducted with US adults in 2019 showed that the number of US adults owning a smartphone or tablet, or using social media, increased significantly between 2012 and 2019 across all generations (PEW Research Center). Surprisingly, the oldest generation seems to be digitising the most rapidly: the share of mobiles and the Internet almost doubled in the group surveyed (born on or before 1945) with up to 28% using social media and 40% using smartphones, although younger generations are more digitised overall. For instance, a GSMA report (2015)—based on consumer surveys in the UK—indicated that 22% of smartphone users spend more than 3 h a day accessing the Internet from phones and that, within 5 years, approximately half of the respondents said they expect to be using their mobile phone to perform daily tasks. In terms of information sharing, 22% expected to share information with their doctor, 76% felt comfortable sharing data about their shopping and purchasing needs, and 75% felt comfortable sharing data about personal interests and preferences, in exchange for deals and other benefits. Having recognised a gap in research on the behavioural factors affecting an active and propagating attitude towards the use of new technologies for risk analysis in the life insurance sector, the authors initiated and conducted a survey on the Polish market. A survey was created using Google forms and redistributed through social networks. It was completed by 304 people, among whom 15% (45 people) were aged over 60, 45% (137 people) aged 40–59, 38% (117 people) aged 20–39, and 2% (5 people) aged under 20. Among the respondents, 61% were women and 39% were men. The vast majority of the respondents had received a higher education (238 correspondents, 78% of the respondents), with the remainder having a secondary education (59 people) or a vocational and basic education (7 people). The research included two questions on sharing data with an insurance company, covering the conclusion of a contract to reduce the insurance premium and obtaining

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consent to monitor selected data during the insurance coverage period to receive additional benefits. In the insurance purchase stage, 66% of all respondents stated they would not be willing to share data about themselves with the insurance company. Therefore, 34% would be willing to share data, with 15.5% simply agreeing and 18.5% agreeing only if they received free wearables (smartwatch or a smartband). It is important to note that this willingness did not depend on age—in the older group (60 years and more) the percentage of people who would be willing to provide the insurance company with data on their life parameters via mobile applications was 31%, among those aged between 40 and 59 this percentage was 36%, and among the youngest respondents (under 40 years) this percentage was 32%. A difference, however, arose in relation to whether the person already used (or did not use) mobile applications in a smartphone, smartwatch, or smartband to record data such as physical activity, sports, life parameters (such as heart rate, respiration rate), and sleeping patterns. Among the respondents, only 41% used these applications; however, among these, 45% said they would agree to provide the insurance company with information about their life parameters in exchange for the possibility of lowering their insurance premium when purchasing life insurance, with 20% of these people expecting to receive a free device from the insurance company. The reluctance to share data with an insurance company may be attributed to the fact that the vast majority of respondents held a university degree. This may indicate a greater awareness of the possibility of further use of the data by insurance companies, which may have a positive or negative impact on the amount of the insurance premium or the desire to purchase another insurance product. A lack of consent to share data collected via mobile applications on health and life parameters, physical activity, and lifestyles was primarily due to the need to maintain privacy (see Fig. 10.3). People participating in the survey were even less willing to be monitored by the insurance company during the insurance period. Among all respondents, only 26% would permit the insurance company to monitor their life parameters and/or physical activity and/or lifestyle and/or health conditions through the mobile application during the insurance coverage period (more: Kwiecień et al. 2019). However, it is worth noting the types of information that respondents would be willing to provide to insurance companies. Overall, 45% would agree to monitor vital signs, physical activity, lifestyle, and health status. Other respondents would agree only on selected aspects (see Fig. 10.4).

10.6

Closing Remarks

To summarise, underwriting is one of the crucial activities performed by an insurance company. The financial condition of an insurance company, the profitability of its products, and the stability and solvency of an insurance company depend on the effectiveness of underwriting. The underwriting process has evolved over the last

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due to privacy reasons

2% 9%

due to fears of raising the insurance premium

10%

other

7% 72%

no answer

due to privacy reasons and due to fears of raising the insurance premium

Fig. 10.3 Reasons for not accepting the transfer of data from mobile applications to insurers when purchasing insurance. Source: Own elaboration based on survey all only physical activity life parameters, physical activity vital parameters, physical activity, health condition only vital parameters physical activity, lifestyle vital parameters, physical activity, health condition physical activity, lifestyle, health condition physical activity, health condition only health condition

0%

5%

10%

15%

20%

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35%

40%

45%

50%

Fig. 10.4 Type of data respondents would agree to share with the insurer during the policy period. Source: Own elaboration

century. Demographics and new technologies now pose major underwriting challenges. Demography, including a transformation in the age structure, implies a need to reformulate the method and scope of information analysis and digitisation. The development of new technologies, including access to medical and health information through applications on a smartphone or smartwatch, provides new opportunities for insurers to develop underwriting tools to collect and process data. To capture all the opportunities offered by digitisation, carriers need to avoid pitfalls and make important shifts in their approach to business transformation. They need to resist the temptation to pursue the latest “shiny object” or dozens of digital initiatives in parallel. Instead, they should prioritise and sequence their digital initiatives and adopt an agile way of working strategically to build and then rapidly

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launch digital capabilities. Most carriers need to more than quadruple their investments in digital systems and adopt two-speed IT to make sufficient progress without this being slowed by the need to replace legacy systems (McKinsey 2016). It should be emphasised, however, that surveys of social attitudes—including those by the authors—indicate that the level of acceptance regarding sharing data with the insurance company is low, especially for monitoring during insurance coverage. It is therefore essential to pay attention to this aspect, which can be a development buffer, and place an emphasis on education, raise awareness of the potential benefits, and eliminate the threats perceived by potential customers. In the opinion of the authors, the technologies also provide an opportunity to cover the old-age group, which generates a difficult risk. However, the use of underwriting monitoring during insurance coverage will help build databases for the next few years while also offering related services such as prevention and early medical diagnosis, limiting the insurer’s risk. This is important, given the inefficiency of pension systems (lack of adequacy and pension gap), which may constitute an area for further exploration in relation to life insurance. Finally, it is also important to note the threat such huge opportunities for data collection generate, such as a lack of privacy and discrimination, because these can shift the traditional axis of information asymmetry to the detriment of the insured person.

References Atkins, D., & Bates, I. (2008). Insurance. London: Global Professional Publishing. Benett, C. (2004). Dictionary of insurance (p. 310). London: Pearson Education, FT Prentince Hall. Billings, P. R., Kohn, M. A., de Cuevas, M., Beckwith, J., Alper, J. S., & Natowicz, M. R. (1992). Discrimination as a consequence of genetic testing. American Journal of Human Genetics, 50, 476–482. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1684266/. Binder, S., Gancia, P., Mattone, D., Ramezani, S., Ring, I., & Straub, M. (2018, June). A vision for European life insurance: The time for bold actions has come, insurance practice. McKinsey and Company. https://www.mckinsey.com/industries/financial-services/our-insights/a-vision-foreuropean-life-insurance-the-time-for-bold-actions-has-come#. Błajda, J., Barnaś, E., & Pieniążek, A. (2017). Rola mobilnych aplikacji medycznych w profilaktyce wybranych chorób, Praca poglądowa. (Role of mobile medical applications in the prophylaxis of selected diseases). Instytut Położnictwa i Ratownictwa Medycznego, Uniwersytet Rzeszowski, Instytut Ochrony Zdrowia, Państwowa Wyższa Szkoła Zawodowa w Tarnowie, 2017. https://doi.org/10.20883/ppnoz.2017.14. Brown, A. E., Thomas, J. N., & Bosselman, H. R. (2015). Are they leaving or staying: a qualitative analysis of turnover issues for generation Y hospitality employees with a hospitality education. International Journal of Hospitality Management, 2015(46), 130–137. https://doi.org/10.1016/j. ijhm.2015.01.011 Costonis, M., Starrs, A., & Viale, E. (2018). Redefine your company based on the company you keep. Intelligent insurer unleashed. How do you improve the way people work and live? Technology vision for insurance. Accenture. https://www.accenture.com/_acnmedia/pdf-79/ accenture-technology-vision-insurance-2018.pdf. Dimock, M. (2019). Defining generations: Where Millennials end and Generation Z begins. Retrieved from http://www.pewresearch.org/fact-tank/2019/01/17/where-millennials-end-andgeneration-z-begins/.

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Eling, M., & Lehmann, M. (2018). The impact of digitalization on the insurance value chain and the insurability of risks; Geneva Papers, 2018. The International Association for the Study of Insurance Economics, 43(359–396), 1018–5895/18. Retrieved August 26, 2019, from, https:// www.researchgate.net/publication/321636110_The_Impact_of_Digitalization_on_the_Insur ance_Value_Chain_and_the_Insurability_of_Risks. Ernst and Young. (2015, August). Introducing ‘pay as you live’ (PAYL) insurance, insurance that rewards a healthier lifestyle. London: Ernst and Young. https://www.ey.com/Publication/ vwLUAssets/EY-introducing-pay-as-you-live-payl-insurance/$FILE/EY-introducing-pay-asyou-live-payl-insurance.pdf. European Commission. (2017, November). The 2018 ageing report, underlying assumptions and projection methodologies. GSMA. (2015). Mobile connect regional consumer research report: EU. https://www.gsma.com/ identity/wp-content/uploads/2015/10/Mobile-Connect-consumer-research-report_EU.pdf. Guzel–Szczypiórkowska, Z., & Visan, J. (1996). Ocena ryzyka w ubezpieczeniach życiowych, w : Ubezpieczenia życiowe, red. Warszawa: Doan O., Poltext. https://buy.garmin.com/pl-PL/PL/p/603201. https://www.amodo.eu/porsche-releases-pay-how-you-drive-phyd-cash-back-concept/. https://www.garmin.com/en-IE/performance-data/running/#physiological-measurements. IAA. (2014). Underwriting around the world. The underwriting sub-committee of the international actuarial association mortality working group (IAAMWG). http://www.actuaries.org/cttees_ tfm/documents/mwg_singapore_item9_underwriting_around_world.pdf. Joly, Y., Knoppers, B. M., & Godard, B. (2003). Genetic information and life insurance: a ‘real’ risk? European Journal of Human Genetics, 11, 561–564. https://www.nature.com/articles/ 5200998. Klein, A. (2013). Life insurance underwriting in the United States – yesterday, today and tomorrow. British Actuarial Journal, 18(2), 486–502. https://doi.org/10.1017/S1357321713000196 Kwiecień, I., Kowalczyk-Rólczyńska, P., & Popielas, M. (2019). Pay as you live and new technologies in life insurance underwriting in the context of generations characteristic and attitudes - evidence from poland; proceedings of the 34th international business information management association conference (IBIMA), vision 2025: Education excellence and management of innovations through sustainable economic competitive advantage; ISBN 978-09998551-3-3. Łańcucki, J. (2018). Klient na cyfrowym rynku ubezpieczeniowym. Prawo Asekuracyjne, 2(95), 2018. McKinsey. (2016). Harnessing the power of digital in life insurance. McKinsey and Company. https://www.mckinsey.com/~/media/McKinsey/Industries/Financial%20Services/Our% 20Insights/Harnessing%20the%20power%20of%20digital%20in%20life%20insurance/ Harnessing-the-power-of-digital-in-life-insurance.ashx. Mkrtychev, S., & Enik, O. (2018). Automated underwriting control in a regional insurance company. In Advances in economics, business and management research, volume 47, international scientific conference “Far East con”. Paris: Atlantis Press. Mould, A. (2003). Implications of genetic testing: discrimination in life insurance and future directions. Journal of Law and Medicine, 10(4), 470–487. Mutai, J., Bii, H., & Kiplang, J. (2017). Knowledge-based system for life insurance underwriting. International Journal of Information Technology and Computer Science, 9(3), 40–49. Kenya. Nabholz, C., & Rechfeld, F. (2016). Seeing the future? How genetic testing will impact life insurance. Swiss re Centre for global dialogue. Retrieved October 3 2019, from https://www. researchgate.net/publication/306434259_Seeing_the_future_How_genetic_testing_will_ impact_life_insurance. Poprawska, E., & Jędrzychowska, A. (2016). In W. Ronka-Chmielowiec (Ed.), Działalność bezpośrednia zakładów ubezpieczeń w: Ubezpieczenia (2016). Warszawa: C. H. Beck.

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Rekosz, M., Stanisławska, K., & Sowulewska, A. (2018). Cyfryzacja sektora ubezpieczeń w Polsce 2018. Raport Accenture i PIU. https://www.accenture.com/_acnmedia/PDF-90/Accentureraport-o-cyfryzacji-sektora-ubezpieczen-w-polsce.pdf#zoom¼50 Sovie, D., Curran, J., Schoelwer, M., & Bjornsjo, A. (2017). Dynamic digital consumers. Everchanging expectations and technology intrigue. Dublin: Accenture. https://www.accenture. com/t00010101t000000__w__/au-en/_acnmedia/pdf-41/accenture-pov-dynamic-consumersaustralia.pdf. Spano, N. (2019) Spano Natalie: How digitalization of life insurance will transform a static industry. Retrieved August 26, 2019, from https://www.ibm.com/blogs/insights-on-business/ insurance/how-digitalization-of-life-insurance-will-transform-a-static-industry/. Vaughan, E. J. (1982). Fundamental of risk and insurance. New York: Wiley. Vaughan, E. J., & Vaughan, T. (2008). Fundamental of risk and insurance (10th ed.). Hoboken: Wiley.

Chapter 11

Innovation in Life Insurance: The Economic Landscape and the Insurance Distribution Directive Adam Śliwiński and Pierpaolo Marano

11.1

Introduction

Innovation, which is defined as the “beating heart of the economy of the twenty-first century”, is one of the key elements of economic policy and competitiveness. Innovations always require a new “knowledge” and high “organizational efficiency”. Thus, they are stuck in the corporate “intellectual capital” (human, organizational, and relative). Innovations may occur only in those companies where this capital actually exists and is reflected in the competences of the managerial staff. Together with entrepreneurship creates a set of factors conducive to the economic development. Innovations are also challenging for the insurance sector. On the one hand, sector itself can be innovative, with the latter supporting innovative activities of other entities by reducing the degree of risk aversion of these entities. The aim of the paper is to discuss whether life insurance sector is innovative and what is the impact of regulation on that phenomenon. The paper consists two main parts. First part focuses on the idea of innovation within financial sectors starting from definition of innovation activity then moves to an analysis of innovation within financial markets which describes the research conducted by Silber and Barras. In general the authors of the paper assume that insurance markets are now in the first phase of revers Barra’s cycle (improved efficiency phase—it means that the markets innovates within new technology). This part aims at evaluating if the life insurance market is innovative in terms of Pearson’s secondary product innovation rather than on primary product innovations (innovates within distribution channels, etc.).

A. Śliwiński (*) Warsaw School of Economics, Warsaw, Poland e-mail: [email protected] P. Marano Università Cattolica del Sacro Cuore, Milan, MI, Italy © Springer Nature Switzerland AG 2020 M. Borda et al. (eds.), Life Insurance in Europe, Financial and Monetary Policy Studies 50, https://doi.org/10.1007/978-3-030-49655-5_11

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The second part focuses on an analysis of the recent regulation issued by the European Union, that is, the Directive 2016/97 of 20 January 2016 on insurance distribution (IDD). The IDD posted the innovation on life insurance within the scope of product design and distribution that is now regulated mainly with the set of rules known as product oversight and governance (POG). Thus, the paper investigates if the changes expected from the implementation of the POG may lead to some of the product innovations listed by Pearson.

11.2

The Concept of Innovation in Financial Services

Insurance belongs to financial services. Insurance activity however is specific and unique on the map of other services. It is because insurance is a particular tool of handling risk, especially life insurance. One of the main features that distinguish insurance is randomness value of claims paid only when cover risk is realized. In terms of life insurance the long-term nature is also crucial. The insurance could also be distinguished because of the following features: • • • • •

Financing from collected premiums A problem rather with valuation of liabilities not assets Long-term nature (fixed price for the long-term contract) Separation of offering from the entity offering Financial liability on both sides of the insurance contract (insurer to pay claim, client to pay premium)

However in terms of insurance, innovation probably goes the same way how all kind of innovation in services go. That part of the paper focuses on an idea of innovativeness among services in comparison to production. Innovativeness is an idea extensively defined in the literature (Schumpeter 1960, p. 104; Whitfield 1979; Niedzielski and Rychlik 2006, p. 19; Janasz and Kozioł 2007, p. 14). The main theory of innovation was created by Schumpeter. By innovation he means the changes in the methods of production and transportation, production of a new product, change in the industrial organization, opening up of a new market, etc. The innovation does not mean invention rather it refers to the commercial applications of new technology, new material, new methods, and new sources of energy. However, these definitions generally refer to processes in kind and not to financial services (Sliwinski et al. 2015). That is probably because the research on the innovativeness in the financial sector is relatively less often than with regard to the manufacturing sector and production processes. Very interesting and worth to mention here is the research conducted by Laeven et al. The research deals with the modelling of financial decisions made in connection with the valuation of the level of innovativeness of entrepreneur. The Schumpeterian definition of innovation however is applied. Therefore innovation is assumed with connection to new product takeoff. The problem of innovativeness is researched in terms of financial

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services; nonetheless, it referred to the services mainly by the assumption that offering new and profitable products by entrepreneurs in the manufacture area may be an important factor to maximize profits also in the financial sector (Sliwinski et al. 2015). Innovation in finance is understood as a diverse kind of improvement delivered to the final costumer like new procedures or new ways of offering. The researchers wondered whether innovations in the financial sectors are a condition for and factor of the sustainable economic growth. On the other hand they touched also the sociological problem connected to the innovations as a tool helping to manage individual risk by identification of the way and degree innovativeness helping to manage that risk. Modern studies of innovation have focused almost exclusively on the experience of manufacturing industry. They have emphasized technological opportunities for innovation and have suggested that these are exogenous determinants of the structure and competitiveness of an industry. Financial innovation has remained until recently a virtually unexplored area of economics. One of the first was W.L. Silber who in 1983 examined monetary innovations, such as new credit instruments and investment contracts, in US capital markets since 1952 (Silber 1983). The research conducted by him of relationship between financial supervision and innovation has become the focus of financial sector research. Based on the study of financial supervision and innovation index of commercial banks, Silber establishes a coupling model for financial supervision and innovation of commercial banks and conducts an empirical research based on data contained in annual reports of commercial banks listed in the form of A Shares. The results obtained showing that financial supervision and innovation of domestic listed commercial banks are at the stage of moderate coupling, upon which relevant suggestions are concluded. He found a positive correlation between such innovations and money market constraints relating particularly to the level and volatility of interest rates. Regulatory constraints and technology (information processing and data transmission) were also found to be important factors inducing the creation of new monetary products. The next studies which are very interesting from the perspectives of differences of innovation process within services in comparison to manufacturing are studies published by Barras. The results obtained by Barras inspired the authors of the paper to think about the nowadays innovativeness of life insurers. Barras has examined the impact of IT on financial services such as insurance and retail banking since the 1960s. From this he has constructed a theory of services innovation based on a reversal of Schumpeterian product cycle theory. Table 11.1 presents so-called Barras’s services innovation reverse cycles. According to Barras there is an interaction between new technologies in the capital goods industries (IT manufactures) and innovation in the implementing service industries. The interaction occurs by way of the two product cycles working in opposite directions, so that manufacturing innovation moves from an emphasis on product to process. In terms of services innovation does the reverse. Thus as product innovation declines in industry, it accelerates in services. The question arises if nowadays life insurance sector is innovative in terms of reverse Barras’s cycle. To answer that question we need to distinguish two types of

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Table. 11.1 Barras’s service innovation reverse cycle A: The product cycle in manufacturing 1. Introduction/takeoff phase Major product innovation clusters during establishing off new industries 2. Growth phase Standardization of products—falling unit costs—competition shifts to process innovations to improve decreasing range of products 3. Maturity phase Markets nearing saturation—shift towards more incremental process improvements to reduce unit costs 4. Transition phase Established technology/industries become increasingly obsolescent/vulnerable to competitors from new technology/industries B: Barras’s reverse product cycle in services 1. Improved efficiency phase Initial investment in new technology (IT) by established firms leads to incremental process innovation to improve efficiency of delivery of existing products—to boost labour productivity and reduce costs 2. Improved quality phase More radical process innovation to improve effectiveness/quality of existing products 3. New product phase Competition shifts to product differentiation: new firms, new products, and new markets Source: Based on Pearson (1997, p. 237)

innovation. That was interestingly done by Pearson (1997, p. 238). He diversified innovation process connected to financial services as innovations connected to products and innovations connected to processes. Process innovation has been defined as a change in the process of producing existing lines of insurance, for example, improvements in risk assessment (new policy conditions, new classifications of existing risks), in marketing, and in organization. Product innovations are further divided into two groups. First group is connected to the creation of new products covering new risks. That one is called primary product innovation (PPI). Primary product innovation depends on technological combination of external economy. However secondary product innovation (SPI) could be interpreted as a creation of new products for existing risks. Therefore according to Pearson, secondary product innovations depend solely upon process innovations within the insurance sector and do not need any external stimulus. Kinds of insurance innovations distinguished by Pearson are presented in Fig. 11.1. Product innovation connected to appearance of new risk is much more sophisticated in comparison to process innovation and also secondary product innovation. This is especially true in terms of life insurance that are perceived as traditional products. In a history there were much more innovations in terms of products for non-life insurance (Table 11.2) comparing to life insurance (unit-linked) (Table 11.2).

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Fig. 11.1 Kind of Pearson’s insurance innovations. Source: Based on (Pearson 1997)

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Insurance innovations Process innovations

Product innovations Primary product innovations

Secendary product innovations

Table 11.2 Product innovation in insurance, 1720–1900, by date and country of introduction Type of insurance Automobile Boiler Burglary Credit Cycle Electrical machinery Elevator/lift Employer’s liability Engine Engineering Fidelity guarantee Hailstorm Licence Livestock Loss of salary Mortgage guarantee Parcel post Personal accident Plate glass Professional indemnity Property owner’s indemnity Public liability River transport Traveller’s luggage Treaty reinsurance Unit linked Windstorm/tornado

Europe (country and date) UK 1896 UK 1854 UK 1846 UK 1820 UK 1883 UK 1897 UK 1888 Belgium 1848 UK 1872 UK 1858 UK 1840 Germany 1797 UK 1890 Germany 1720 Germany 1892 UK 1888 UK 1883 UK 1848 France 1829 UK 1896 UK 1897 France 1829 Germany 1765 UK 1851 France, Belgium 1820 UK 1957 France 1887

Source: Own elaboration based on Pearson (1997, p. 239), Melville (1970)

USA (date) – 1866 1878 – – – – – – 1876 1870 – – 1892 – – 1864 1874 – – – 1849 1870 – 1952 1861

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Product innovations give the insurer an opportunity to play an important role in contribution to sustainable development on a macroeconomic scale, or even more— on the global scale (Sliwinski et al. 2017). In terms of life insurance, the creation of unit-linked product could be an example of some kind of innovation action done in terms of PPI. Unit-linked product could be very simply defined as a combination of pure risk and speculative risk by applying a unit trust principle to each premium paid in parallel to insurance rules. The unit-linked insurance has appeared in the USA in 1952 and UK in 1957; however, the impact on the markets has been visible in 1962–1963 (Melville 1970). The aim of the paper is a discussion if life insurance sector is innovative and in what extent. However taking into consideration the above discussion about kinds of innovation and innovation cycle, it could be concluded that life insurance sector is not so innovative especially in terms of PPI. One of the simple facts that supports that statement is an analysis of ranking of the top five innovators in insurance industry published by A Medium Corporation, USA,1 where life insurance is mentioned just by one company: Carpe Data. The company provides risk assessment for P&C (property and casualty) and life insurers. Using information extracted from social media, online content, wearables, and connected devices, the company aims to predict the outcome of introducing new products. The next part of the paper looks at the life insurance innovation from the perspectives of regulations that posted the innovation on life insurance within the scope of the set of rules known as product oversight and governance (POG). Thus, the paper investigates if the changes expected from the implementation of the POG may lead to some of the product innovations listed by Pearson and, if so, to which of them.

11.3

Innovation in the Life Insurance in the Context of IDD

One of the most important goals, if not the main, pursued by lawmakers after the financial crisis of 2007/2008, consists of rebuilding the confidence of the investors into the financial markets (G-20 2008). In this respect, the post-crisis regulation aims to prevent any regulatory arbitrage, thus avoiding incentives to set up products that meet the sole purpose of circumventing more stringent standards. Therefore, the alignment of the regulations on banking, insurance, and financial markets shall be requested when there are the same assumptions, regardless of the operator’s qualification as bank, insurer, or investment firm. The levelling of the playing field has attracted insurance products, especially those relating to life insurance and financial ones. This attraction has occurred at international level, where the G-20 played a leading role after the financial crisis (Liedtke 2011), and it is mirrored at EU level with a mutual comeback between the

1 https://medium.com/go-weekly-blog/the-15-most-innovative-companies-in-insurance5533466cde48 (checked 17.12.2019).

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two levels (Marano 2017a). The Directive 2014/65/EU of 15 May 2014 on markets in financial instruments (MiFID II) repealed the previous Directive 2004/39/EC (MiFID). Recital 87 of MiFID II outlined that investments involving contracts of insurance are often made available to customers as potential alternatives or substitutes to financial instruments subject to this Directive. To deliver consistent protection for retail clients and ensure a level playing field between similar products, it is important that insurance-based investment products are subject to appropriate requirements.2 These products satisfy investor needs similar to those satisfied from financial products and therefore raise comparable investor protection challenges. However, differences in market structures and product characteristics between insurance-based investment products and financial ones make it more appropriate that detailed requirements are set out in the ongoing review of Directive 2002/92/EC on insurance mediation (IMD) rather than setting them in MiFID II. The proposal to recast IMD was tabled for adoption as part of a wider “consumer retail package” consisting in the proposal on product disclosures, i.e. PRIPs and UCITS V. The proposals intended to address cross-sectorial inconsistencies which, combined with a low level of cross-border purchasing, is detrimental to consumers. Since the original proposal in 2012, six compromise proposals have been prepared with a view to reaching an agreement on the Council’s general approach. The Directive was renamed Insurance Distribution Directive (IDD) to reflect the change in focusing on the regulation of distribution products. The IDD was adopted by the European Parliament on 24 November 2015 and by the Council of the European Union on 14 December 2015 and published in the EU Official Journal on 20 January 2016. The IDD introduced some rules and principles contained in the final version of the MiFID II. Thus, MiFID II affects insurance regulation in the EU, especially regulation concerning life insurance. We are in front of the phenomenon called as “mifidization” of EU insurance law affecting (1) the sources of the regulation on insurance; (2) the design and distribution of the insurance products; and (3) customers’ protection. Court’s interpretation of the rules on life insurance could be affected too (Marano 2017a, b). With reference to the design and distribution of insurance products, the set of rules on product oversight and governance (POG) is one of the major innovations, if not the most significant, introduced by IDD. POG appeared in the IDD as a mere “copy and paste” from MiFID II, even if some slight differences are found (Marano 2019). POG rules as detailed in the Commission Delegated Regulation (EU) 2017/ 2358 call manufacturers3 for a “product approval process” covering the 2

An insurance-based investment product is any insurance product which offers a maturity or surrender value and where that maturity or surrender value is wholly or partially exposed, directly or indirectly, to market fluctuations. 3 Manufacturers are insurance undertakings, as well as intermediaries that manufacture any insurance product for sale to customers. Insurance intermediaries shall be considered manufacturers where an overall analysis of their activity shows that they have a decision-making role in designing and developing an insurance product for the market (see Article 3 of Commission Delegated Regulation (EU) 2017/2358).

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maintenance, operation, and review of product oversight and governance arrangements for insurance products and for significant adaptations to existing insurance products before those products are brought to the market or distributed to customers, as well as rules for product distribution arrangements for those insurance products (Marano 2019). The product approval process shall contain measures and procedures for designing, monitoring, reviewing, and distributing insurance products, as well as for corrective action for insurance products that are detrimental to customers.4 The product approval process shall be set out in a written policy (“product oversight and governance policy”), which shall be made available to the relevant staff (Marano 2019). Manufacturers shall regularly review their product approval process to ensure that the process is still valid and up to date, and they shall amend the product approval process where necessary.5 The relevant actions, which are taken by manufacturers in relation to their product approval process, shall be duly documented, kept for audit purposes, and made available to the competent authorities upon request.6 The set of rules on POG can therefore be qualified as a discipline of the process inherent in the design and distribution (also) of life insurance products. POG innovates in the process, but it is not just a process innovation because it necessarily reflects on the result of the process, i.e. the product created and distributed. This statement calls to justify two aspects: POG as process innovation and product innovation. With reference to process innovation, all the manufacturers had their own processes for the design and manufacture of insurance products, as well as rules on their distribution, before the introduction of the POG. The innovation introduced by the POG mainly concerns the purpose such process must pursue. Since Solvency II stated that the main objective of insurance supervision is the protection of policyholders and beneficiaries, POG is consistent with this principle by allowing supervisory authorities to anticipate customer protection at the design of the products rather than to the distribution only. POG calls both supervisors and manufacturers to shift to a forward-looking approach similar to that requested under Solvency II. POG discloses to the supervisory authority the persons/units involved in these processes, the way in which the products are manufactured and the purposes actually pursued with these products by the insurance undertakings. The advance knowledge of these processes is functional to an early intervention by the authorities, if they realize how the products or processes are likely to be detrimental to customers (Marano 2020). Innovation in the process, therefore, consists of being customer oriented when designing the products (Marano 2019). Manufacturers need to have transparent and auditable product approval frameworks for new products ensuring that the product approval process is not compromised as a result of commercial, time, or funding

4

See Article 4 of Commission Delegated Regulation (EU) 2017/2358. See Article 4 of Commission Delegated Regulation (EU) 2017/2358. 6 See Article 9 of Commission Delegated Regulation (EU) 2017/2358. 5

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pressures, allows for review and challenge by the compliance, risk, and legal functions, and is not undermined by senior management over-ride. The governance around the launch of new products cannot be overly focused on the profitability of the product rather than meeting identified investment needs for customers in the target market. Manufacturers should identify a target market not only for generating ideas for products including factors that made potential products attractive to customers (and could be used to market them more successfully) but to ensure that products (1) address specific investor needs and (2) are designed in a way that the customer can understand. Stress testing and modelling has to be done minimizing statistical bias that could adversely influence a customer. The innovation above involves the distribution process being the POG a “circular process”, that is, a process requesting manufacturer and distributors to take care of their customers on ongoing basis. Thus, manufacturers are requested to monitoring distribution. Manufacturers cannot take assurances from distributors at face value, but they need to have sufficient information to satisfy themselves that distributors’ policies and procedures were appropriate for their product and target market. In addition, manufacturers have to provide assistance where sales were conducted through banks, without relying on the assumption that banking staff in sales functions had the necessary product knowledge. This is extremely important in the model of bancassurance, which is the predominant distribution channel in life insurance of most of the EU Member States. It is a serious warning for the entities involved in this model because firms need to ensure that their chosen distribution channels have enough information to form an adequate understanding of their products. More generally, regarding the information to distributors, the ongoing due diligence performed by manufacturers on distributors cannot inhibit their ability to check that products are reaching the target market. With reference to product innovation, the set of rules on POG affects both the products manufactured after the entry into force of the new rules and the products existing at that date, in the event of their substantial changes. POG does not concern a specific life insurance product. Thus, it is apparently neutral with respect to the assumptions of innovation identified by Pearson (PPI v. SPI). However, neutrality should not be confused with indifference to products. Neutrality means that POG does not discriminate between risks in life insurance. All life insurance products both embedding new risks and existing risks must comply with POG, that is, the arrangements that set out appropriate measures and procedures aimed at designing, monitoring, reviewing, and distributing products for customers, as well as taking action in respect of products that may lead to detriment to customers. On the other hand, POG aims at designing and distributing products that satisfy the interests and needs of customers belonging to the target market and to whom those specific products are distributed. POG integrates the customer’s protections to the point of sale as it anticipates the protection of customers with respect to poorly designed products. A well-designed and appropriately distributed product is probably not an innovation in the proper sense. However, this seems like an excellent result that IDD intends to achieve.

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11.4

Conclusions

At the age of innovation, the insurance markets cannot remain insensitive to the need for innovation not only in the area of insurance operation of companies but also in the area of products. The aim of the paper is to discuss whether life insurance sector is innovative and what is the impact of regulation on that phenomenon. Assuming the information provided and the analysis done in the main body of the paper, a few conclusions could be written here. First of all the life insurance sector is in the first phase of reverse innovation cycle. It means that the insurers focus mainly on efficiency of delivering products to the clients. However the regulations (POG) impose obligations and incentives to innovate in product sphere. Although, like it was stated, POG does not concern a specific life insurance product and is neutral with respect to the assumptions of innovation identified by Pearson in the near future, it should enhance insurers to innovate in terms of improvement of quality of products.

References G-20 Leaders. (2008). Summit—leaders’ statement, Washington, DC. Retrieved from http://www. nytimes.com/2008/11/16/washington/summit-text.html?pagewanted¼all. Janasz, W., Kozioł, K. (2007) Determinanty działalności innowacyjnej. Warszawa: PWE. Liedtke, P. M. (2011). Insurance activity as a regulatory object: trends and developments and their appreciation in the context of post-crisis global market. In P. M. Liedtke & J. Monkiewicz (Eds.), The future of insurance regulation and supervision. A global perspective (pp. 7–23). London: Palgrave Macmillan. Marano, P. (2017a). Sources and tools of the insurance regulation. In P. Marano & M. Siri (Eds.), Insurance regulation in the European Union: Solvency II and beyond (pp. 5–30). London: Palgrave Macmillan. Marano, P. (2017b). The “Mifidization”: The sunset of life insurance in the EU regulation on insurance? In Liber amicorum in honour of Ioannis K. Rokas (pp. 219–234). Athens: ΝΟMIKΗ ΒIΒΛIΟΘΗKΗ. Marano, P. (2019). The product oversight and governance: Standards and liabilities. In P. Marano & I. Rokas (Eds.), Distribution of insurance-based investment products. The EU regulation and the liabilities (pp. 59–96). Cham: Springer. Marano, P. (2020). The contribution of product oversight and governance (POG) to the single market: A set of organizational rules for business conduct. In P. Marano & K. Noussia (Eds.), Insurance distribution directive: Promises and reality. Cham: Springer. Melville, G. (1970). The unit-linked approach to life insurance. Journal of the Institute of Actuaries (1886–1994), 96(3), 311–367. Retrieved from www.jstor.org/stable/41140123 Niedzielski, P., Rychlik, K. (2006) Innowacje i kreatywność. Szczecin: Wydawnictwo Naukowe Uniwersytetu Szczecińskiego. Pearson, R. (1997). The insurance industry 1700–1914. Economic History Review, L, 2, 235–256. Schumpeter, J. (1960). Teorie rozwoju gospodarczego. Warszawa: PWN. Silber, W. L. (1983). The process of financial innovation. The American Economic Review, 73, 89–95.

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Sliwinski, A., Karmanska, A., & Michalski, T. (2015). Insurance innovation assessment model – process based approach (Part I). Bangkok: ToKnowPress. isbn: 978–961-6914-13-0, http:// www.toknowpress.net/ISBN/978-961-6914-13-0/papers/ML15-233.pdf?. Sliwinski, A., Karmanska, A., & Michalski, T. (2017). European insurance markets in face of financial crisis: Application of learning curve concept as a tool of insurance products innovation – discussion. Journal of Reviews on Global Economics, 6, 404–419. Whitfield, P. R. (1979). Innowacje w przemyśle. Warszawa: PWE.

Chapter 12

Internet of Things (IoT): Considerations for Life Insurers Aleksandra Małek

12.1

Introduction

Like many other industries, the insurance industry has been affected by the Internet of things (IoT). This is a worldwide trend that consumers and enterprises stay connected1 and are present online more often than at any time in the past. This connectivity is enabled by smartphones, laptops, tablets, e-readers, or computers. It is also enabled by other items like dedicated work/route tracking devices, car built-in dongles, home appliances, smart TVs, personal hygiene items, toys, smart clothing items, shoes, and many others. At the same time, patients worldwide are using medical devices connected to their mobiles. Smartwatches and wristbands are replacing traditional watches, allowing their users to track activities, number of steps, or heart rate. The aim of this paper is to provide an overview of IoT in insurance and address current issues, opportunities, and challenges in the life and health insurance area, focusing on applications of wearables, health mobile apps, and medical devices. Due to editorial restrictions, the author will focus primarily on IoT-linked life insurance products and IoT applications at various stages of a typical life insurance value chain. Limited attention will be paid to technical aspects (connectivity types, IoT platform types) as well as data protection and security topics. Those aspects are still relevant but can easily be addressed as part of any cross-industry IoT considerations, without focusing on insurance.

1 Connectivity is ensured by radio or Ethernet protocol. Radio protocols include ZigBee, Zwave, Bluetooth, Wi-Fi, cellular technology, or LPWA (low power, wide-area), and each of them has different characteristics. More information on technical aspects of IoT protocols can be found in (Sinclair 2017, pp. 195–199).

A. Małek (*) Independent Researcher, Warsaw, Poland © Springer Nature Switzerland AG 2020 M. Borda et al. (eds.), Life Insurance in Europe, Financial and Monetary Policy Studies 50, https://doi.org/10.1007/978-3-030-49655-5_12

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References to this article are mainly press releases, reports published by reinsurers and consulting companies, or information obtained directly from life and health insurers. To anticipate potential concerns of reviewers or future readers, it is important to make it clear that the academic literature available is very limited.2 This is a remarkable misalignment between the academia and the market because, at the same time, most of the leading consulting companies and reinsurers agree that the IoT is one of the top trends for insurers.3 The author has conducted comprehensive analysis of those reports and papers published throughout years 2017–2019. Reports prepared by consulting companies are not an academic literature; the scope of information is usually of high degree of granularity and primarily targeted to address questions on how insurers can apply it to reduce their costs, optimize efficiency and operating models or improve customer experience. Research papers published by reinsurers are usually more detailed and more comprehensive.

12.2

IoT-Linked Insurance

12.2.1 IoT in Insurance: Overview IoT has been used in the insurance industry for more than 20 years (Carbone 2017; Kuryłowicz 2016, p. 130) since the first motor telematics policies were sold. As the 2 Throughout May–July 2019, the author conducted a structured and standardized search of the relevant academic literature by searching for the key words “(Internet of things or IoT) and (life insurance),” “(Internet of things or IoT) and (health insurance),” “wearable(s) and insurance,” “Garmin and insurance,” “Fitbit and insurance,” “Telematics and Life Insurance,” “smartwatch and insurance” in the databases: Academic Search Ultimate (EBSCO), Business Source Ultimate (EBSCO), and JSTOR, accessed at the Warsaw University Library. The vast majority of search results were press releases, notes in non-reviewed magazines or newspaper articles. Author has ignored any sponsored articles, any SME briefings shorter than 1 page, summaries of reports provided by consulting companies and reinsurers (any reports the author is referring to in this article are original reports available from those companies) and any juridical articles referring to legal acts binding in the USA. Next, the author has searched the Google Scholar database, using key words “life insurance internet of things,” “life insurance IoT,” “telematics life insurance,” and “insurance wearables.” The vast majority of articles found focused on general IoT considerations, mainly in the security and data protection area, without focusing on life insurance. Some of the articles focused on car telematics pricing. Relevant academical literature has been found, i.e., in the US National Institutes of Health’s National Library of Medicine (NIH/NLM) (https://www.ncbi.nlm.nih.gov/pmc) and in the open access-journal Sensors, available at https://www.mdpi.com/journal/sensors 3 As insurers generally perform limited research of insurance markets or trends, these are the reinsurers and leading consulting companies who provide regular and comprehensive analysis of the insurance markets, trends, risks, opportunities and challenges, often in partnership with research institutes. Examples of such analysis are: (EY 2019; Deloitte 2018; KPMG 2019; McKinsey 2019a, c; Accenture 2018; CapGemini 2017; PWC and Centre for the Study of Financial Innovation (CSFI) 2019; Munich Re 2018a; Swiss Re 2019; Scor 2018a).

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Connected insurance

Personal insurance

Life

Commercial insurance

Non-life

Examples (not exhaustive):

Connected life insurance (PAYL –

Telematics car insurance

Workers compensations

‘pay as you live’)

Smart home insurance

Ships insurance

Connected health insurance bundled

Connected health insurance 6

Cargo insurance

with life insurance

Crop insurance (smart farms)

Business interruption

Critical illness

Property insurance

Long term care /

Fleet insurance

/Life care plans 5

Fig. 12.1 Scope of IoT applications in insurance industry. Author’s view

connectivity technologies have flourished and the number of connected devices has exploded, the applications of IoT have expanded. IoT-linked insurance is known as “connected insurance” or “insurance of things.”4 Figure 12.1 presents an overview of possible applications, at the product type level. Connected insurance is used in both personal and commercial lines. In the personal insurance business, the IoT applications are used by both life and non-life insurers, covering variety of risks and products. In life insurance, multiple options of connected life and health products are offered. IoT is also redefining long-term care products, as daily-assisted living services are using IoT to monitor health conditions of policyholders and properties of those who are living unassisted. In the non-life area, IoT is still used in motor insurance; however multiple sensors, mobile apps, and connected devices are also redefining traditional home insurance. While commercial insurance stays outside of the scope of this article, only several illustrative examples are presented in Fig. 12.1. More detailed information is commonly available; one of the most recent analysis of IoT applications for commercial insurers was published by McKinsey in June 2019 (McKinsey 2019b). Typical IoT insurance products will be presented later in this chapter, focusing on connected car, connected home, and connected health and life. Even if the main topic of this article is applications of IoT for life insurance, it is reasonable to provide a high-level analysis of connected car and smart home products. This is because of the following reasons: • IoT in motor insurance has been offered for more than 20 years. Both product scope and servicing have evaluated to elevate customer experience. It is important ‘Telematics’ is another word commonly used for IoT. Some IoT practitioners use the work ‘telematics’ to cover all types of IoT products while some other reserve this word for connected car policies. 4

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to understand the offering approach as it can easily be adapted to life and health IoT products. • While the market is growing, the insurers will likely be offering a complex IoT coverage to improve customer experience,5 i.e., managing and self-servicing multiple IoT insurance products via one mobile app or Web customer dashboard.

12.2.2 Connected Car Insurance The “usage-based insurance” (UBI) naming6 was first developed for car telematics insurance to better reflect the fact that the premium was related to the style and frequency the policyholders used their cars. This product is also known as PAYD (pay as you drive) insurance. Currently many telematics policies consider not only the mileage, frequency, and area of the car that is usually used but also the driver’s behavior and their driving style. So this is no longer a PAYD concept but more a PAYH (pay how you drive) philosophy. Nowadays the drivers are also being advised on what they could do better to drive more efficient and safer. Policyholders would also receive real-time alerts on traffic, dangerous weather, etc. This is an important move from offering a pure product to providing product-related, valueadding services. Thus, the insurer is now present is policyholder’s lives more often than in the past. And this is what customers expect from their insurance companies: they want their insurer to be an everyday insurer (Accenture 2017a; Capgemini and EFMA 2018). This is a huge paradigm change and a new challenge for insurance companies as historically they were present in their client’s lives at the stage the police was sold and then when the claim was reported. Connected insurance can use the following devices to collect information and data: 1. Car hardwired built-in devices (“blackboxes”) 2. Self-installed devices plugged into vehicle’s onboard diagnostic ports (OBD-II in the USA or EOBD in Europe) 3. Dedicated mobile applications (apps) 4. Other devices like ride tracking cameras (sim card equipped)

5

Subject to basic regulatory requirements to run the life and non-life insurance business separately. As the market has evolved, the UBI is probably not the best name (anymore) to name such policies. Moreover, in most cases, the premium is usually not adjusted to incorporate mileage or driving behavior of the policyholders—as per estimations of IoT Insurance Observatory, in 2017 this applied for 9% of the policies only. This means that the remaining 91% of policies don’t adjust premiums and the driver/policyholder pays a precalculated premium for the whole policy period. Where the behavior/mileage data is used, is only the renewal of the policy. The IOT Insurance Observatory is a think tank, gathering executives from more than 50 insurers, institutions, and the Internet of Things ecosystem. Full list of members is available on organization’s website: https://iotinsobs.com/. 6

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Depending on the option applied, the data can be sent to the insurer (and the car producer if applicable) automatically or may require user’s manual submission. Telematics sensors have become more available and affordable, they are also easier to use than in the initial phase of the market. Vehicle telematics devices usually measure actual car usage parameters (mileage, driving duration, and frequency, time of the day the car is usually used), trip data (types of road, specific routes, location), and driving style (speed, hard braking, acceleration rate). It is also possible to track driver’s behavior or mood (including negative emotions like road rage) (Deloitte 2016). Tracking driver’s behavior and providing guidance on how to drive safer is a simple way of implementing loss prevention. Safer driving leads to lower number of accidents and crashes; it also reduces the loss frequency (Kuryłowicz 2016, pp. 134–135). Some insurers offer discounts and special offers on non-insurance services and products. Many IoT applications offer more personalized services, such as monitoring of teens or elderly drivers (Deloitte 2016): real-time tracking of geo-localization (parental control on where their teen kid is communing), monitoring of driving behavior and controlling some features or settings while someone else is driving user’s car. As different customers have different expectations, the key success factor here is to consider that and make the most of this fact by enabling more personalized usage of IoT solutions and products.

12.2.3 Connected Home Insurance Connected home insurance is using the concept of so called “smart home.” Smart homes are homes that use various sensors, devices, electronics, appliances, or complex systems which are trackable via mobile apps, directly, or via smart home hubs. Depending on the device/smart home solution, the following advantages can be achieved: • Property-related cost reduction (examples: thermostats) • Early warning of possible accident or damage (examples: leakage sensors, smoke alarms, damp detector, break-ins) • Improvement of life quality (examples: turning off the light remotely using mobile app, vacuum or washing machine activated via mobile app, etc.) • Better time management (examples: an oven that can be turned remotely on the way home, a refrigerator that keeps a track of the stock through barcode or RFIT scanning) • Safety improvement (in-door and outdoor cameras, move sensors, camera built-in doorbells, smart door locks) • Entertainment (examples: smart refrigerators including Wi-Fi-connected screens that enable diary keeping, shopping list updates, browsing of popular culinary blogs)

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In case of a smart home ecosystem, the sensors are compatible and integrated with the smart home hub. They usually also support voice commands through intelligent virtual assistants.7 Those agents are accessible from various devices. For example, the Google-provided smart home system Nest supports voice commands through Google assistant via Android phone, Google Home speakers,8 headphones, etc. Ecosystem is not always required. Depending on the option, a single sensor may also be compatible with an external hub, virtual assistant, or mobile app. Sensors and smart home ecosystem solution provide instant control of insurer property. The users are notified in real time (via mobile app or text messages) where the system has suspected any kind of dangerous situation (water under the dishwasher pipes or washbasin, smoke in the kitchen, attempt to open the door while the houseowners are at work, etc). Insurance-wise, the sensor-led real-time risk alerting means efficient and ongoing prevention activity. In many cases the smart home system can prevent the occurrence of the insurance event: many of the sensors are precise enough to alert the user about the possible danger before it has occurred. And in cases where an insurance event has occurred, the severity will likely be reduced as the real time alerts allow prompt reactions. So far, several options of connected home insurance have been launched in the market. The most common options are presented below. 1. The insurer offers complimentary smart home sensors/devices or gets in partnerships with smart home providers to offer discounts on smart home devices/ kits. This approach has been applied by the following companies:

7 It is important to explain the role of intelligent virtual assistants at this stage as they are relevant for IoT value chain, i.e., insurance distribution which will be covered in later chapter of this article. Intelligent virtual assistants (sometimes called intelligent personal assistants) are voice controlled software agents that are able to proceed the requested action like searching the Internet, schedule alarms, order a pizza, order a taxi, turn on the music, change the lightening, text someone, send an email, etc. It interprets user’s commands based on natural language processing and speech recognition and then performs required action based on software/apps programmed on-/ downloaded to this device. They need a so called “wake word” to start understanding and processing the request. Those wake words differ from one virtual assistant to another. For example, for Amazon’s Alexa the wake-up word is “Alexa” while Google assistant “wakes up” when the words “OK Google” or “Hey Google” are used. Apple’s virtual assistant is woken if a phrase “Hi Siri” is heard. 8 Google Home® Speaker is a smart speaker that uses the Google assistant’s functionality and can proceed user’s command within the functionalities/apps built-in/downloaded on the device. It uses the same natural language processing and voice recognition functionalities as the Google assistants commented in footnote above. However, it is an independent device that looks like a speaker and in fact it is a speaker. Other smart speaker are also available, like Amazon’s Echo, or Apple’s HomePod. They are available in selected countries. The functionalities can vary from one country to another. https://store.google.com/gb/?hl¼en-GB&countryRedirect¼true.

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• The Travelers have agreed partnership with Amazon so the Amazon’s smart home kits could be offered at discounted price.9 • The American Family Insurance has partnered with Nest to provide complimentary smoke and carbon monoxide alarms (American Family Insurance 2015). • The UK-based insurer Neos offers complimentary devices and the Neos mobile app.10 2. Policyholders pay for their smart home devices/systems and receive premium discounts when reported back to their insurance provider. This approach has been supported by Next Safety Rewards developed by Google, as explained in the text box below. 3. Peer to peer options are also available, where the neighborhood, community, or friends and family are alerted via text message/chats that a sensor has a detected a fire or burglary attempt, so they can react by checking the situation onsite and call fire brigade, police, or ambulance if needed. Example The smart home provider Nest has developed so-called Nest Safety Rewards program11 that helps obtaining premium discounts from cooperating insurance companies. After the permission has been granted by the policyholder, the Nest provider sends regular smart home summarized data to the relevant insurance company. Rather than sharing detailed data, they share the minimum data required to prove that the smart home protection is working. This includes status of the batteries, smoke sensor, carbon monoxide sensor, and connection to the Internet. This is the same information that is visible for the smart home owner on their monthly home report. It also includes information on postal code/ZIP code and the names of the rooms where the devices are installed.

12.2.4 Connected Life and Health Life and health connected insurance policies are sometimes referred to as “pay as you live” (PAYL) insurance. Multiple product options are available and they will be

9

The discount is 25$ per kit, 3 kit variants are available, the Amazon’s personal assistant speaker “Echo” is offered for free. Valid as of June 2019, offer available on Amazon’s website at www. amazon.com/travelers, retrieved 20/06/2019, 4:20 CET. 10 Terms and conditions are available on insurer’s website at www.neos.uk. Complimentary device offer valid as of May 2019, confirmed by Neos consultant in personal web chat with the author of this article on 23-May-19. Transcription of the conversation available per request. 11 Terms and conditions are available on Google Nest Help Centre, accessible at https://support. google.com/googlenest/answer/9242091?hl¼en. Scope of information provided to participating insurance company is confirmed accordingly.

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analyzed later in this chapter. At this stage, it is important to understand the way such insurance products are “connected.” This is primarily possible via dedicated mobile apps and so-called wearables.

12.2.4.1

Wearable Technology

Wearables are electronic devices with various types of sensors that is worn on-body, in-body, near-body or in-clothing (European Commission 2017, pp. 2–5; Yetisen et al. 2018; Spender et al. 2019, p. 3). This category includes not only smart wearables (smartwatches, fitness trackers, wrist bands, sport watches) as we know it. It covers also medical devices, smart clothing and footwear, jewelry, accessories; smart contact lenses and electronic tattoos are other examples of wearables. The following types wearables are significantly relevant for life insurance applications: • Fitness wearables (fitness trackers and smartwatches) • Medical devices sharing health data in real time They are analyzed later in this section. Most fitness wearables are wrist-worn but some of them can be clipped to the clothing or shoes, some can be worn on pendants, there are also heart rate trackers that are worn on the chest. There also fitness trackers jewelry (necklaces, rings, bracelets, earrings) and earbuds and headphones. Fitness wearables vary on the easiness of use, battery types, scope of physical activities trackable, or scope of additional features sensor (Aroganam et al. 2019; Tedesco et al. 2017). Fitness trackers are primarily developed to track health conditions (sleep, pulse) and physical activity (number of steps taken, physical activities) of the user. They usually look like a bracelet and sometimes are called smart wristbands or smart bands. As the scope of functionalities has increased, some of them are capable to get vibration alerts for all notifications, including calls, text messages, etc. Some producers like Garmin distinguish between fitness trackers and activity trackers. Smartwatches are more advanced; most of them have GPS built-in and music play control option. Call or text message notification is a common functionality. Some devices are able to make SOS calls. In addition, Apple has recently equipped their newer smartwatches with electrocardiogram and fall detectors (Apple 2019). They can work independently or paired with a smartphone, and they look like a watch.12 Athletes (including amateur athletes) can choose from wide range of smartwatches and fitness trackers to track specific physical activities (running, 12

Devices can be compared directly on producers’ websites: Xiaomi: https://www.mi.com/uk/list/ #5, Fitbit: https://www.fitbit.com/compare, Apple: https://www.apple.com/uk/watch/, Garmin: https://buy.garmin.com/en-GB/GB/c10002-p1.html?sorter¼featuredProducts-desc, Samsung: https://www.samsung.com/uk/wearables/ Availability and functionalities may vary by country.

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triathlon, etc.).13 Usually such devices provide guidance or insights on training plans; they also compare user’s activity and health statistics (sleep length, steps taken, floor levels climbed) against other users. Also, various types of insights and coaching tips are available for the specific users, based on their present and past activity. Devices can be synchronized with user’s mobile phone or laptop, where dedicated dashboards are available. Functionalities of wearables are constantly upgraded to provide users with more functionalities (like all-day stress tracking, body’s battery reserve, or relaxation breathing timer) to encourage to wear them more often. The most advanced wearables include touchless payment functionalities14; they are also able to record stress lever or track the menstrual cycle and fertility windows, allowing female users to log symptoms, moods, and cycle regularity. Fitness wearables are gaining on popularity. The retail sales of fitness wearables have increased significantly within the last 6 years. As per estimations of the Euromonitor,15 the total number of wearables purchased by retail customers in 2018 is estimated as almost 135.7 million items.16 The retail shipment in year 2013 was lower than 7.2 million items (Table 12.1). The main producers of those devices in 2018 were Xiaomi Inc., Apple Inc., Fitbit, Samsung Corp, and Garmin Ltd. (Euromonitor 2019). Medical Devices Medical devices are used to support diagnosis or monitor a disease already diagnosed (Spender et al. 2019, p. 3). Diabetes tracking devices are a good example of how the market has evolved, offering multiple non-invasive glucose self-monitoring solutions, using dedicated devices (DIA-VIT®), patches (SugarBeat®) or earclips (GlucoTrack®). There is a wide range of dedicated smartphone apps available that track glucose levels and analyze their fluctuations during the day. They also remind their users to check glucose level and take medicines and recommend to handle certain symptoms. Some

13

Author of this article has been using Garmin Vivoactive HR that is capable to track running, cycling, in-door running, swimming, golf, walk, rowing, skiing, and many other physical activities. 14 More information on Garmin Pay and Fitbit Pay is available on producers’ websites: Garmin: https://explore.garmin.com/pl-PL/garmin-pay/, FitBit https://www.fitbit.com/fitbit-pay. 15 For purposes of Euromonitor-provided market estimations a category of wearables has been limited to electronic devices designed to be worn by the user, typically on the wrist or head. The category only covers products designed for retail sale and consumer usage. Products designed for use in medical, military, and any other profession such as diving are excluded. 16 Estimations provided by different analysts are not consistent because of different methodologies used and different definitions/scoping of wearables. As per IDC estimation, the total number of wearables shipments in 2018 was estimated as 172.2 million units, with 46.2 million items sold by Apple. However, IDC’s analysis covered also other wearables like ear-worn wearables and as per IDC’s clarification this market segment is growing. For more detailed information on IDC’s estimations please (IDC 2019) access IDC’s report linked under: https://www.idc.com/getdoc. jsp?containerId¼prUS44901819&utm_medium¼rss_feed&utm_source¼Alert&utm_ campaign¼rss_syndication.

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Table 12.1 Market size of wearables, estimated as number of wearables distributed to retail customer (in thousand on units) World Asia Pacific Australasia Eastern Europe North America Western Europe Middle East and Africa Latin America

2013 7189.2 41.4 79.3 5.9 5855.3 1183.7 23.2 0.4

2014 29,930.9 9136.9 1670.9 280.6 14,639.3 4088.1 99.5 15.7

2015 80,247.6 35,000.4 2408.1 822.3 31,233.2 10,032.9 451.5 299.2

2016 106,707.0 46,907.8 2868.9 1427.3 38,524.6 14,764.8 1145.2 1068.4

2017 123,778.0 55,030.6 3289.1 2403.6 41,881.7 17,506.2 1950.4 1716.3

2018 135,685.4 60,701.1 3549.5 3738.8 43,727.5 19,336.9 2765.5 1866.1

Source: Consumer Electronics. Euromonitor from trade sources/national statistics (www.portal. euromonitor.com/portal/researchsource/tab). Accessed in Warsaw University Library in May 2019 (https://www.buw.uw.edu.pl/zasoby-online/bazy-online/#P, (access restricted to registered library users)

of them include insulin calculators and database of nutritional information on food and beverages. There have been attempts to start tracking glucose level from sweat, saliva, or tears, with the most prominent attempts made by Google and Novartis to launch glucose tracking smart contact lenses.17 Also multiple cardiovascular conditions are supported by connected medical devices. They are aimed at conditions monitoring and illness prevention. While connected via patient app, they are able to monitor pulse, blood pressure, and blood oxygen. Some of the devices enable the user to do the real time self-assessment of heart rhythm and identify any abnormalities like bradycardia or tachycardia, based on the personal medical-grade EKG. Those recording can be then shared with the doctor or clinic directly. Real-time ECG is accessible while electrodes are attached to the chest and are connected to user’s smartphone via cable (example: CardioSecur’s offer); moreover, it can be also completed based on user’s finger sensors instead of usually used wire and patches (AliveCor’s product). In more advanced cases, the ECG device can be enhanced with digital stethoscope and patient’s condition can get livestreamed to their doctor (example: Eko Health Inc’s product).18

12.2.4.2

Smartphone Applications (Apps)

As per estimations of Research2Guidance, a Berlin-based strategy advisory and market research company specialized in mHealth market analysis, in 2017 there

17

Smart lenses developed by Google and Novartis have been dismissed by many researches as ‘technically infeasible’ as tears have proved not as reliable in measuring glucose levels in humans compared to extracting blood. See more at: https://labiotech.eu/features/contact-lens-glucose-diabe tes/. 18 More details on referenced devices can be found here: Cardiosecur https://www.cardiosecur.com/ , AliveCor https://www.alivecor.com/, EkoHealth https://www.ekohealth.com/.

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were 325,000 mHealth (a.k.a. m-health) apps available on major app stores that were downloaded 3.7 billion times (Research2Guidance 2017, p. 10). This category includes both apps connected to wearables and those wording independently. It covers both fitness apps and “pure” medical apps, covering infotainment and lifestyle, fitness and wellness, people monitoring and safety, and medical and healthcare. Medical apps provide multiple features like connecting to doctors, measuring heart circulation and blood, meditation techniques, monitoring, or self-managing diseases like diabetes, mental health, heart diseases, blood hypertension, bowel diseases, etc. Medical research is familiar with mHealth and latest trends and concepts; multiple apps have been analyzed by medical researchers/ practitioners with assessment and outcomes presented in medical journals.19 While lot of apps are available free of charge, there is a bunch of premium apps which charge users offering extra services like personal trainer, heart rate analysis, or prime customer service. Some of them (like Endomondo) offer both options: while the basic version is complimentary, the premium one charges the users.

12.2.4.3

Pays as You Live (PAYL) Products: Overview

Multiple PAYL products are being offered: • Life or health policies linked to wellness and health platforms • Life insurance with managed conditions and regular underwriting • Health policies with real time health tracking, incl. Telemedicine The most popular option is that the policy is “connected” to dedicated health and wellness program Vitality that collects, tracks, and analyzes information on policyholder’s lifestyle, physical activity, checkups, diet, and moving pattern. This information is based on wearables paired, most of the data (like medical checkup or blood test result information is put manually by the user). By implementing various reward schemes and premium discounts, PAYL insurance providers encourage their policyholders to stay active and track their physical activity, eat well, and get regular checkups. Usually, with wearable-linked offer the customers earn points for healthy behavior. Depending on the product, customers may also be rewarded if certain health parameters or conditions (like BMI) have improved. They are offered discounts on

When searching the PubMedCentral® (PMC) provided by the US National Library of Medicine National Institutes of Health using the query “medical apps” the search engine resulted in more than 73.7 thousand of publications. PubMed Central® (PMC) is a free full-text archive of biomedical and life sciences journal literature at the US National Institutes of Health’s National Library of Medicine (NIH/NLM) (available https://www.ncbi.nlm.nih.gov/pmc) (retrieved on 16-May-19, 12 AM CET). The query “mHealth” resulted in over 19.3 thousand research items.

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non-insurance services like gym membership, organic food stores, etc. Another special offers or exclusive discounts are also available, even if not related to nutrition of physical exercise.20 Some of the plans promote long-term measured physical activity. For example, in the John Hancock’s life insurance policies, offered with Vitality program,21 if the user has achieved the platinum threshold for the period of 3 years, they can enjoy free Amazon prime membership for 1 year (John Hancock 2018). The program provides relevant support by giving access to expert fitness and nutritional resources; they are also able to achieve personalized goals by using dedicated mobile apps or personalized dashboard on the website. Other examples of insurance companies that collaborate with Vitality are AIA (Australia), Generali Leben (Germany), Manulife (Canada), Ping An Health (China), Sumitomo Life (Japan). Another type of insurance of thing are term life and critical illness policies with kind of continues underwriting which is applied for a pre-existing condition. Adding continuous or regular underwriting could be a way to offer the insurance product to those who would normally be declined because of their healthy issues or obesity (Swiss Re 2015; Spender et al. 2019). The regular underwriting approach has been applied by multiple companies, including two UK-based insurance companies: • The Exeter: applies for the “Managed Life” product, suitable for customers with high BMI or type 2 diabetes • The Royal London: applies for the Diabetes Life Cover, suitable for customers with type 1 and type 2 diabetes Insurers require checkup of the managed condition on policy anniversary. In case of improvement the premium may be decreased, as per conditions agreed with the customer (The Exeter 2019; The Royal London 2019). Those examples are still relevant for the IoT discussion even if they don’t apply continuous IoT underwriting directly [compare considerations given in (Spender et al. 2019)]. Given the variety of mhealth devices and mobile apps, those conditions could be easily managed automatically and more often providing ongoing underwriting. This applies not only to diabetes or BMI but also to other diseases where condition improvement is measurable. Thus in particular cases, using connected devices could also lead to insurability extending. 20 John Hancock’s life insurance policies with Vitality Plus membership provide the policyholders with discounts on hotel booking via partnered booking provider Hotels.com. 21 This applies for policy option with Vitality Plus only (monthly cost of 2USD), it doesn’t apply for basic, free of charge option with Vitality Go. Vitality is the provider of Vitality program, the leader in this market segment. It works in partnerships with insurers and employers around the world, this led to more than eight million people from 19 countries being included in the program (Vitality Group 2018).

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Real-time tracking of health condition is relevant for health insurance providers. Early diagnosis saves lives and cuts treatment costs. Insurance companies have been contributing to worldwide trend of telemedicine popularity.22 Telemedicine enables the policyholder to receive quicker diagnosis or advise from the doctors, via videocalls or calls, also using livestreaming of some of the medical tests, like electrocardiogram (ECG). Some insurers, like Oscar Health, have built their own telehealth services, while other insurance companies cooperate in partnership with dedicated telehealth providers. For example, Bupa is working with Babylon Health, and Cigna has been in cooperation with MDLive; MeMD provides services for Aflac.23 As described above, health insurance providers are aiming to engage and monitor their policyholders based on information available of their wearables, mhealth devices, or dedicated apps. On top of using ready-to-go platforms like Vitality, some companies are also looking to build their own wellness and health programs/ applications that combine the wellness/lifestyle information with medical records and medical advice or recommendation already received. Aetna has recently launched a program Attain,24 which combines information included in Aetna’s health record with Apple Watch activities. Alerts and insights displayed by this mobile app include not only reminders to move to achieve step or activity targets. Users are also reminded to get a flu shot and visit their doctor for checkup of refill a prescription. The Attain app includes database of various medical providers (blood labs, MRI providers, hospitals, etc.). Thus the policyholder can easily find a recommended provider in their vicinity and arrange a visit. Aetna’s policyholders can participate in multiple challenges like nutrition challenge or building a bed time plan. For all those activities and challenges users are rewarded with points to earn a new Apple Watch or to exchange them for gift cards from popular retailers (CVS Health 2019). The long-term coverage is another area where IoT is being applied, i.e., for tracking health conditions of elder or disabled people and reducing the coveragerelated expenses. As some of the wearables now offer functionalities to monitor real-

22

Detailed information on telemedicine and virtual health trends, including insight gathered from the patients, can be found in Accenture’s report “Voting for Virtual Health” published in 2017 (Accenture 2017b). Another view on healthcare trends is provided by McKinsey in their publication “Next-generation member engagement during the care journey” (McKinsey 2019c) and by Deloitte in the 2019 Global Health Care Outlook (Deloitte 2019). 23 (1) Overview of Oscar’s telemedicine is available on insurer’s website: https://www.hioscar.com/ doctor-on-call (retrieved on 21-Sep-19, 11:13 CET); (2) Bupa UK’s and Babylon cooperation was announced in Bupa’s News, released on 30-Nov-2018 (Bupa 2018). (3) Dedicated website for Cigna Customers using MDLive services is available under: https://www.mdliveforcigna.com/ mdliveforcigna/landing_home (retrieved 21-Sep-19, 11:30 CET); (4) MeMD’s telemedicine services have recently been rewarded by the insurer; corresponding press release is available on MeMD’s website: https://blog.memd.me/aflac-recognizes-memd-partner-year-outstanding-service/ (retrieved 21-Sep-19, 11:35 CET). 24 Dedicated mobile app is available from Apple Store (https://apps.apple.com/app/apple-store/ id1438040517).

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Marketing

Product development

Underwriting and pricing

Distribution

Policy servicing & claims

Fig. 12.2 Insurance value chain (simplified). Source: Author’s view

time ECG, detect a fall, and make a SOS call they could be used for ongoing monitoring of policyholders. As per CNBC’s news revealed in January 2019 (CNBC 2019),25 Apple had been in discussions with several US healthcare providers investigating how Apple Watches could support policyholders, i.e., seniors at risk.

12.3

Impact of IoT on Life Insurance Value Chain

IoT impacts all areas of the insurance value chain, as illustrated as Fig. 12.2. This section will analyze impact of IoT at various stages of the value chain.

12.3.1 Marketing and Product Development As the customer has decided to share their data, a connected product should give them much more than just a coverage and competitive pricing. This is more like a packaged offer: on top of the insurance coverage, the insurer provides monitoring, coaching, and offer rewards from ecosystem partners. Some insurers offer discounts on wearables or offer a complimentary device. Of course, identifying a trend is one thing but implementing it and making it profitable and attractive for the customers in another thing. The outcomes of several customer studies conducted within last 5 years make it clear that the customers expect not only premium discounts but also value-added services from their insurers, so they can feel being incentivized to share their data.26 Those surveys were mainly focused on auto-telematics policies, but it sounds reasonable to adjust them also for life and health policies. Key points in developing connected L&H product is to address basic questions on product coverage (life insurance type, riders), target customers, and underwriting (scope of underwriting, acceptance and decline criteria, potential management of pre-existing conditions using connected medical devices). Another set of questions is related to the wearables themselves (type/brands of wearables participating in the program) and their offering (possible options are no wearable offered, free wearable 25

The full story can be watched at https://www.cnbc.com/2019/01/15/apple-talking-to-privatemedicare-plans-about-subsidizing-apple-watch.html (watched on 20-Sep-19, 7:10 PM CET). 26 The relevant consumer surveys are quoted are (LexisNexis 2016; Accenture 2017a; Capgemini and EFMA 2018).

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offered for participants, wearable offered with price discount, wearable monthly fee depended on physical activity level). What kind of wellness platform/mobile app will be used for the program and how the customers are rewarded are another important question that need to be addressed. If the customers are eligible for premium discounts based on their activity, then this should be decided together with Marketing, when the product is being developed and clearly communicated to the customers.

12.3.2 Risk Underwriting and Pricing 12.3.2.1

General Remarks

Traditionally, when applying for life insurance the applications have been asked by their future life insurers to complete pre-defined questionnaires or surveys with the aim to provide their potential insurers with relevant information on their weight, occupation, lifestyle and health condition, i.e., illnesses occurred (also the family history in some cases). While mortality tables remain a baseline for any mortality estimations, such additional information enables a more accurate risk assessment related to an individual applying for life insurance policy. The underwriting procedures vary by insurers; the scope of information sought depends not only on the country but also the reinsurer covering the portfolio or policy (if applies). The common underwriting options are: • A simple declaration on general health condition; if the customer is not able to sign it because of certain questions, this is possibly meriting further investigation. • An underwriting questionnaire, including health conditions, medical history, lifestyle habits, occupation, etc. • An underwriting questionnaire and undergo medical examination (usually blood and urine sample tests, blood pressure, ECG, chest X-ray, etc.) When applying the IoT in underwriting of life insurance, the general approach doesn’t change. It means that the individual’s medical and lifestyle information are still used to assess the risk and better adjust insurance premium. What would change is the scope and type of information that can be used the frequency of risk assessment. Traditional underwriting questionnaires usually don’t include questions on numbers of steps taken, numbers of vigorous or moderate activity hours per week or sleep length and intensity.27 Such data is usually collected by wearables and if insurers want to make use of it, they need to build relevant models or update existing ones. So far there the following approaches have been taken to address this issue:

27

On rare occasions you can see general questions on physical activity.

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• There have been research attempts undertaken to establish correlation between physical activity, steps taken, and sedentary workstyle and mortality with the aim to build it into the risk assessment models. Munich Re’s analysis will be presented later in this section. • Some insurers have tried to build into their risk assessment so-called biological age that can be calculated based on some data collected by wearables and sensors. The biological age approach will be presented along with SCOR’s value proposition. • Another approach is to create health scores, based on data collected by wearables. Given complexity of underwriting and pricing strategies and technical editing limitation of this article, health scores methodologies will not be presented in this article.

12.3.2.2

Munich Re’s Evaluation of Effectiveness of Physical Activity in Stratifying Mortality Risk28

Munich Re has recently evaluated the effectiveness of physical activity in stratifying mortality risk. Their study was based on the dataset provided by the health analytics company Vivametrica29 who had compiled data from several clinical research studies conducted on US population samples between 1998 and 2004. The dataset used included not only demographic information and measurements of BMI, waist size, blood pressure, and cholesterol. Dataset covered also indicators of cancer, diabetes, and cardiovascular status (physician-assessed), and family history of diabetes and cardiovascular disorders. The dataset was enhanced with information on smoking status, alcohol and drug use, and step count and minutes of moderate to vigorous activity. Before Munich Re applied their simulations and calculations, Vivametrica had provided sample weights per each individual. This was based on several variables including age, gender, and geographical location. Next, the reinsurer had built a simulated insurance portfolio by applying some typical underwriting and financial limitation. In total, the portfolio included 8173 individuals (2125 with vital status “death” and 6048 with vital status “alive”) for whom Munich Re have estimated standard actuarial experience analysis: portfolio’s mortality ratios were compared against the mortality of the total US population as per Human Mortality Database US Life Tables.

28

This section summarized evaluation taken by Munich Re and the characteristics of the dataset provided by the health analytics company Vivametrica, as described in Munich Re’s publication stratifying mortality risk using physical activity as measured by wearable sensors Munich Re (2018a, b). 29 Vivametrica is a health analytics company and provides dedicated tools to structure or analyse health data. One of the tools developed is vScore Life which Vivametrica’s mortality risk assessment toolkit. Vivametrica has worked with MunichRe and Scor, but it collaborates also with wellness platform providers. Company’s website is available under https://www.vivametrica.com/.

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The outcome of their studies shows that moderate and higher number of steps per day decreases the mortality and the sedentary behavior causes high mortality. The key findings are: • Physical activity effectively stratifies mortality risk. • Number of steps taken per day may be effective in identifying high mortality risk for sedentary behavior; this indicator is an important predictor of mortality risk. • Steps per day provide additional segmentation of mortality.

12.3.2.3

Biological Age Model Developed by SCOR Global Life

Insurers has used chronological age as a basis to price any life and health insurance products because age is primary risk factor for mortality, diseases, and any detraction of body functions. However, chronological age doesn’t take into account any adjustments about person’s lifestyle, healthy conditions, weight, etc. In contrary, the biological age is more accurate as more “personal,” as it is calculated on the basis of person’s physical and mental condition. Using biological age instead of the chronological is an alternative approach to underwriting. Multiple tools and applications are available online to perform selfassessment.30 Depending on the model, they may include inter alia the following factors/ conditions: • Gender, race, and educational level • Weight-related indices like body/mass index (BMI) or body fat index • Certain diseases history of the individual and their family; family’s longevity • Individual’s medical conditions (blood pressure, blood sugar level, cholesterol etc.) • Nutrition and diet-related information • Number of steps taken and physical activity • Stress factor and sleeping habits • Medication usage and frequency or medical tests taken

30 They differ by scope of input required, formulas used, and complexity of the models. Even if not called “biological age” directly, this concept is also used by Vitality Health assessment, provided by AIA Vitality in Australia. Questionnaire is available on insurer’s website under: https://www. aiavitality.com.au/vmp-au/know_your_health/vitality_health_review.

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The Biological Age Model (BAM)31 has been developed by SCOR Global Life in collaboration with Vivametrica. Recently the reinsurer has partnered with Garmin to obtain more biometric data (SCOR 2018b). The concept of using biological age is another way to encourage the policyholders to stay active and be more aware of their heath. As per SCOR, if policyholder’s biological age is lower than the calendar age, then they can be offered discounts on premium. Adding additional factors like number of steps taken, physical activity, or sleep length to the traditional risk assessment looks reasonable to achieve more accurate underwriting and pricing. Fitness wearables are getting equipped in more advanced functionalities and sensors. Currently the data collected is not yet enough to replace traditional (i.e., not simplified) underwriting queries, like those on illnesses and syndromes the customer or their family members have suffered from. While they are asked to either eliminate illnesses from the coverage or accept them and adjust the premium to the actual risk, they cannot be missed. As the technology continues to develop and wearables are equipped with more features, this could be achieved in the nearer future.

12.3.3 Distribution IoT offering faces the same challenges as other aspects of insurance digitalization: customer centricity, multi-channel offering, product personalization, and ecosystem services. While PAYL are based on behavior tracking devices and apps, they are naturally attractive for all kinds of so-called ecosystem offering. They can easily be advertised in multiple fitness or health apps, in the same way the apps advertise hospitals, health centers, etc. If being displayed as a dedicated offer to some customers, it can be achieved in the same way as typical insights messages. An example of an insight messaging is presented below (red boxed) (Fig. 12.3).32 Acting as part of insurance ecosystem is not limited to offering extra services in addition to insurance policy. This approach is also built on the assumption that insurance can be offered as part of a wider service or product packaging rather than being sold in a separate step. This create product can be offered as part of a complex wearable or health app-related service, distributed by a non-insurance entity. Of course, the insurance product will remain underwritten and serviced by the insurer. A key success factor is to select the optimal partners.

31

The concept of BAM is described in details on SCOR’s dedicated websites: https://www.scor. com/en/biological-age-model-bam (retrieved on 21-Sep-19, 18:58CET) (SCOR 2018b). 32 Insights displayed by Garmin; screenshot includes author’s data, no additional data usage consent is needed, however sensitive data has been removed from the picture.

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Fig. 12.3 Screenshot of an insight provided by Garmin, incl. redirection to external providers where more information can be found

Insurance distribution has become omni-channel. While products are offered in multiple channels, the customer are able to choose on how they want to buy their product and make premium payment or service their policy. Social media has become one of the distribution channels. Buyers can now use Facebook or Messenger to ask for a quote, request information, or buy their insurance or take a selfie to estimate the age rather than putting required information manually.33

12.3.4 Policy Servicing and Claims Management PAYL products have redefined frequency and materiality of the contacts between the policyholders and their insurer. The insurer is present in their daily lives playing a role of the everyday coach or advisor and encouraging policyholders to be more active.

This has been introduced by Zurich UK as ‘Facequote’, see https://www.zurich.co.uk/insurance/ facequote.

33

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Fig. 12.4 (a and b) Screenshot of an insight provided by Garmin (number of steps taken and sleep pattern)

Another important feature is providing insights to device users. User’s activity or sleep pattern is compared against the activity of other users which may act as motivation factor and incentive to become more active. The user can compare their sleep, activity, number of steps taken against other certain age range, gender, or against the total population of users of wearables they use, as presented on (Fig. 12.4). Reminding on regular checkups, prescriptions, or medical tests are another examples of how important and desired the regular interactions could be. Such alerts are typically displayed by mobile applications available for health insurance policyholders. Insurers are also experimenting with intelligent virtual assistant platforms34; their customers can now ask simple policy-related queries, obtain product information, or get a quote.35 As the popularity of those devices is increasing, in the future they can become another standard policy servicing channel. Wearables and connected medical devices are also used in the claims area.

34

Virtual assistants have been described in the smart home insurance section of this article. Examples of insurers “present” on Alexa are Travelers, Liberty, Axa, Aviva, DFV Deutsche Familienversicherung AG, or Liberty mutual. Usually the scope of services provided is limited to obtain simple information on policy, product scope, or contact numbers to insurer’s representatives. 35

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First and the most prominent application is the automatic FNOL (first notification of loss). As the medical devices track and monitor the conditions of people with chronic illnesses, they are able to send automatic alerts to the doctor or nurse if the devices has recorded abnormal or dangerous health episode. Some of the smartwatches are also equipped in emergency SOS feature so the device is able to call emergency services using the phone paired. Also, some of the smartwatches (like Apple Watch Series 4 or later) have been equipped with fall detection features and option to contact emergency services as necessary. In Apple watches, the fall detection feature is automatically enabled for users aged 65 or above (Apple 2019). In April 2019, the Munich Fire Department reported in their press release a case of being alarmed by a smartwatch of an 80-old-year user who had fallen heavily. The smartwatch automatically announced the GPS details the details of the accident to the emergency unit dispatcher (Feuerwehr München 2019). As some lawyers suggest, in some circumstances, the information collected by wearables could be used in litigation processes, i.e., the personal injury or emotional distress cases. In the first case, the data could be used to compare the wellbeing/ endurance before and after the accident or injury. This approach has already been used in Canada, where a lawyer used Fitbit data history to prove that the plaintiff’s activity level after injury had been lower than the threshold expected for a person of her age and profession (the plaintiff was a personal trainer) (Governo and Devlin 2016; Schanerman 2017). In a similar way, information on sleep pattern (i.e., showing insomnia) and stress symptoms (like heart rate variability) collected by wearables can be used to support emotional stress claims. There are also mental health dedicated mobile apps that allow the users to log and track their emotional state, sleep quality, alcohol consumption, etc. Such data can be an evidence in quality of life and emotional distress claims (Governo and Devlin 2016).36 Data collected and logged by wearables or mobile apps can be used by insurance companies to detect potential frauds or unjustified claims. Despite obvious doubts around ethics of using such personal (or intimate) information or data from the wearables, in particular circumstances data can be used by insurers to decline a claim or reduce the compensation.

12.4

Data Security and Protection

Concerns around data using, sharing, and processing is one of the main challenges of using wearables in life and health insurance. However the data security concerns related to wearables are similar to concerns observed for any other connected devices. As data is vulnerable to cyber criminals and fraudsters, IoT security needs to be ensured at three levels: system security, application security, and network

36 Any juridical discussions around accuracy, reliability and legitimacy of the data are not covered by this article.

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security. Possible attacks are limited not only to attacking the network. They may also result in altering the integrity of data or modifying data, disclosing private data, making network available, or sending bulk messages (Abdur Razzaq et al. 2017, p. 387). The European Union’s General Data Privacy Regulation (GDPR) requires enhanced data protection obligations on entities who process or store data; this also refers to insurers. Of course using wearable data by insurers requires respective and comprehensive data consent from customers. However, even of the concerns are the same, the health-related data is more sensitive and in fact is it vulnerable. It cannot be replaced, for example, credit card in case of data breach or hacking (Kellogg 2016, p. 76). As mentioned in previous chapters, wearables are being enhanced to track more health data; some of this data is not only sensitive but also intimate.37 Thus insurers, wearable producers and wellness program providers should pay extra attention to security of the data collected. From the insurer’s point of view, there will always be questions around data reliability and accuracy. The insurers are not able to check whether the device has been worn by their policyholder or by someone else, so they need to be ready for new options of insurance frauds. Data accuracy is also something the insurers cannot control as it depends on the device and user’s walking stride length logged. There may also be breaks in data available due to limited connectivity or weak battery of the device. When launching connected insurance product, insurers need to be ready to address such concerns.

12.5

Summary

Applications of IoT by life and health insurers are another aspect of worldwide, cross-industry trend of applying connectivity in people’s lives. Today’s customers and patients are living in the digital and connected world, so they can expect that their insurers will also get connected. At the same time, policyholders’ expectation is that insurers will be present in their lives more often, advising them and coaching where needed. As the digital native generation is now attacking insurance markets, more policyholders will now expect real-time servicing, dedicated mobile apps, and connected health conditions tracking. Insurance companies are keeping a close watch on those new trends and are more keen using data collected by wearables to improve their underwriting, pricing, or customer servicing. Information on number of steps taken or regularity of physical activity could also be helpful in segmentation of mortality or morbidity. This is important, because, as alarmed by WHO,

37

As part of menstrual cycle tracking, Garmin’s female users can manually log information on their sexual activity or sex drive. It is not compulsory and this option can be disabled by the user without disabling of the female health tracking module.

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insufficient physical activity is one of the leading risk factors for death worldwide and a key risk factor38 for noncommunicable (chronic) diseases39 (NCD). Due to editorial restrictions, this article presents only a high level overview of connected life insurance and applications of wearables. The aim of this paper was also to identify touchpoints between IoT and various stages or a typical life insurance value chain. Given the fact that IoT applications create enormous opportunities for underwriting, pricing and claims this research should be continued. Data protection and security is another important topic and a challenge for IoT in insurance. Again, due to editorial restrictions the considerations given to data usage and protection may look insufficient. While most of those aspects are not insurance specific and are typical for applications of IoT in other industries, it was author’s compromise to focus mainly on PAYL products and application of IoT in insurance value chain which are unique for insurance. Acknowledgements Any opinion presented in this article is author’s private opinion and must not be in any way associated with opinion of author’s employer or any other organization to which the author has provided any kind of work or services as an employee or a contractor. Author is an independent researcher not affiliated to any school. This article doesn’t violate any intellectual rights. It has been produced in author’s private time, using author’s private devices and software. It is not a work product performed for or on behalf of author’s employer. Any information on insurers, reinsurers, analytics health companies, wellness platforms providers, or consulting companies used in this article is a publicly available information, obtained from public website or relevant press releases of these companies. Author declares that no restricted or confidential information has been used in this article. All sources and items quoted or referenced in this paper are available via Google Scholar, Google search, or databases available at the University of Warsaw Library (https://www.buw.uw.edu.pl/). The author declares no conflict of interest. Entities quoted or referenced in this article have been presented in an objective manner based on availability of data and information with the aim to ensure possibly broad and differentiated perspective. This is a research article and must not be read (fully or partially) as any kind of recommendation, offering, advisory, or consultancy. All health data used in this article is author’s personal data. No additional consent is needed to use this data (including screenshots) to explain the IoT applications for life insurers, as covered in this article. However the author doesn’t give consent to use her data further. This applies also for citation or future references to this article.

38

Along with tobacco use, physical inactivity, the harmful use of alcohol and unhealthy diet. Source: WHO, Noncommunicable diseases, Key Facts, https://www.who.int/news-room/factsheets/detail/noncommunicable-diseases (retrieved 13.07.2019, 7:20 PM CET). 39 As per WHO statistics, the NCD’s kill 41 million people every year, causing 71% of the deaths globally. Each year, 15 million of people aged 30–69 die from those diseases (so called pre-mature deaths). Main NCD types are: cardiovascular diseases (stroke, heart attacks), cancers, chronic respiratory diseases and diabetes. Those 4 disease types count for over 80% of all premature NCD deaths. Source: WHO, supra note.

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References Abdur Razzaq, M., Habib, S., Ali, M., & Ullah, S. (2017, January). Security issues in the internet of things (IoT): A comprehensive study. International Journal of Advanced Computer Science and Applications, 8(6). Accenture. (2017a). The voice of the customer. Identifying disruptive opportunities in insurance distribution. Accenture Financial Services 2017 Global Distribution and Marketing, Consumer Study: Insurance Report. Retrieved September 23, 2019, from https://www.accenture.com/ t00010101t000000z__w__/gb-en/_acnmedia/pdf-50/accenture-distribution-marketing-surveyinsurance-report.pdf. Accenture. (2017b). Voting for virtual health. Retrieved September 20–23, 2019, from https:// www.accenture.com/_acnmedia/pdf-43/accenture-health-voting-for-virtual-health.pdf. Accenture. (2018). Accenture technology vision for insurance. Retrieved September 23, 2019, from https://www.accenture.com/_acnmedia/pdf-79/accenture-technology-vision-insurance-2018. pdf. American Family Insurance. (2015, June 17). American family insurance keeps focus on proactive home safety by offering nest protect devices at no cost to Minnesota customers, Press Release. Apple. (2019). Use fall detection with apple watch. Retrieved September 21, 2019, from https:// support.apple.com/en-us/HT208944. Aroganam, G., Manivannan, N., & Harrison, D. (2019). Review on wearable technology sensors used in consumer sport applications. Sensors, 19(9), 1983. BUPA. (2018). Bupa and Babylon to offer 1000s of UK SMEs access to digital health services, News Release, 30 Nov 2018. Retrieved September 23, 2019, from https://www.bupa.com/ newsroom/news/bupa-and-babylon-smes-launch. Capgemini. (2017). Top 10 trends in propert and casualty insurance 2018, 11 Dec 2017. Retrieved September 23, 2019, from https://www.capgemini.com/resources/top-10-trends-in-propertycasualty-insurance-2018/. Capgemini & EFMA. (2018). World insurance report 2018. Carbone, M. (2017, July 31). UBI is a failure but telematics insurance IS working extraordinarily well. Carrier Management. CNBC. (2019). Apple talking to private Medicare plans about subsidizing apple watch. https:// www.cnbc.com/2019/01/15/apple-talking-to-private-medicare-plans-about-subsidizing-applewatch.html. CVS Health. (2019, January 29). Aetna announces attain, a personalized well-being experience that combines health history with apple watch information to empower better health. Press Release. Retrieved September 23, 2019, from https://cvshealth.com/newsroom/press-releases/ aetna-announces-attain-personalized-well-being-experience-combines-health. Deloitte. (2016). Opting in: Using IoT connectivity to drive differentiation. The internet of things in insurance. In A research report from the Deloitte center for financial services. London: Deloitte University Press. Deloitte. (2018). 2019 insurance outlook. Growing economy bolsters insurers, but longer-term trends may require transformation. London: Deloitte. Deloitte. (2019). Global health care outlook. London: Deloitte. Euromonitor. (2019). www.portal.euromonitor.com/portal/researchsource/tab. Accessed in Warsaw University library in May 2019. https://www.buw.uw.edu.pl/zasoby-online/bazy-online/ #P. Access restricted to registered library users. European Commission. (2017, December). Smart wearables reflection and orientation paper. Including feedback from stakeholders. Retrieved September 10, 2019, from https://ec.europa. eu/digital-single-market/en/news/feedback-stakeholders-smart-wearables-reflection-and-orienta tion-paper. EY. (2019, 08 January). Five tech trends that will define future of insurance. Retrieved May 2, 2019, from https://www.ey.com/en_gl/insurance/five-tech-trends-that-will-define-the-futureof-insurance.

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Feuerwehr München. (2019). Smartwatch meldet Sturz einer 80-Jährigen. Retrieved September 19, 2019, from https://www.presseportal.de/blaulicht/pm/131419/4245978. Governo, D. M., & Devlin, S. E. (2016, July). Make that Fitbit a lie detector. Using personal data tracking to investigate claims, claims magazine. IDC. (2019). IDC reports strong growth in the worldwide wearables market, led by holiday shipments of smartwatches, wrist bands, and ear-worn device. https://www.idc.com/getdoc. jsp?containerId¼prUS44901819&utm_medium¼rss_feed&utm_source¼Alert&utm_ campaign¼rss_syndication. John H. (2018, September). John Hancock leaves traditional life insurance model behind to incenitvize longer, healthier lives, Press Release. Kellogg, S. (2016, March/April). Every breath you take: Data privacy and your wearable fitness device, Journal of The Missouri Bar 2016, 76–82 KPMG. (2019). Insurtech 10: Trends for 2019. Retrieved September 21, 2019, from https://assets. kpmg/content/dam/kpmg/xx/pdf/2019/03/insurtech-trends-2019.pdf. Kuryłowicz, Ł. (2016). Usage-based insurance: The concept and study of available analyses. Insurance Review 4/2016/Wiadomości Ubezpieczeniowe 4/2016. LexisNexis. (2016). 2016 Usage-based insurance (UBI) research results for the U.S. consumer market. White Paper, LexisNexis. McKinsey. (2019a, February). Digital ecosystems for insurers: Opportunities through the internet of things. McKinsey. (2019b, June). Next-generation member engagement during the care journey. McKinsey. (2019c, June). Tackling the IoT opportunity for commercial lines insurance. MeMd. (2018, March 6). Aflac® Recognizes MeMD® as partner of the year for outstanding service. Press Release. Retrieved September 21, 2019, from https://blog.memd.me/aflac-recognizesmemd-partner-year-outstanding-service/. Munich Re. (2018a). Stratifying mortality risk using physical activity as measured by wearable sensors, Munich Re. Munich Re. (2018b). Tech Trend Radar 2018 – Technology drives future business opportunities. Retrieved September 28, 2019, from https://www.munichre.com/topics-online/en/digitalisation/ future-technologies-tech-trend-radar-2018.html. PWC and Centre for the Study of Financial Innovation (CSFI). (2019). Insurance Banana Skins 2019 Insurance Banana Skins 2019 The CSFI survey of the risks facing insurers. Research2Guidance. (2017). mHealth app economics 2017/2018. Current status and future trends in mobile health, Report, Berlin. Schanerman, N. (2017, October). Wearable technology & discoverable data, claims magazine. SCOR. (2018a, March). The impact of artificial intelligence on the (re)insurance sector. SCOR. (2018b). Biological Age Model (BAM). Using wearable data to empower healthier lives. Sinclair, B. (2017). IoT Inc. How your company can use the internet of things to win in the outcome economy. London: McGraw-Hill Education. Spender, A., Bullen, C., Altmann-Richer, L., Cripps, J., Duffy, R., Falkous, C., & Yeap, W. (2019). Wearables and the internet of things: Considerations for the life and health insurance industry. Br Actuar J, 24, E22. Retrieved September 29, 2019, from https://doi.org/10.1017/ S1357321719000072. Swiss Re. (2015). Life insurance in the digital age: Fundamental transformation ahead, sigma 6/2015, Zurich Swiss Re. (2019). 2010s: Technology, e-business and cyber risk continue to shape the industry’s future’ Retrieved September 23, 2019, from https://www.swissre.com/institute/research/sigmaresearch/50years/2010s-cyber-risk.html. Tedesco, S., Barton, J., & O’Flynn, B. (2017). A review of activity trackers for senior citizens: Research perspectives, commercial landscape and the role of the insurance industry. Sensors, 17(6), 1277. The Exeter. (2019). Managed life. Policy summary. Retrieved September 21, 2019, from https:// dyn.the-exeter.com/download/brochure?code¼ML-PS.

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The Royal London. (2019). Diabetes life cover. Retrieved September 21, 2019, from https://www. royallondon.com/life-insurance/diabetes-life-cover/. Vitality Group. (2018, November 28). Vitality-linked insurers to get 100 million people 20% more active by 2025, Press Release. Retrieved September 29, 2019, from https://www.vitalitygroup. com/press-release/vitality-linked-insurers-get-100-million-people-20-active-2025/. Yetisen, A, Martinez Hurtado, JL, Ünal, B., Khademhosseini, A., & Butt, H. (2018). Wearables in medicine. Advanced Materials, 30, 1706910. Retrieved September 20, 2019, from https://doi. org/10.1002/adma.201706910.

Relevant Websites Oscar Health: https://www.hioscar.com/doctor-on-call. Dedicated website for Cigna Customers using MDLive services is available under: https://www. mdliveforcigna.com/mdliveforcigna/landing_home. Dedicated website for customer of The Travelers, seeking guidance for smart home insurance terms and conditions https://www.travelers.com/personal-insurance/amazon. Garmin Pay: https://explore.garmin.com/pl-PL/garmin-pay/. Fit Bit Pay : https://www.fitbit.com/fitbit-pay. Aetna’s Attain, link to mobile app in Apple Store https://apps.apple.com/app/apple-store/ id1438040517. Zurich UK, Facequote. https://www.zurich.co.uk/insurance/facequote. Vivametrica: https://www.vivametrica.com/. The IOT Insurance Observatory: https://iotinsobs.com/. Google Home Speaker: https://store.google.com/gb/?hl¼en-GB&countryRedirect¼true. Google Nest: Google Nest Help Centre, https://support.google.com/googlenest/answer/9242091? hl¼en. Neos: www.neos.uk. Xiaomi’s wearable offer: https://www.mi.com/uk/list/#5. Fitbit’s wearable offer: https://www.fitbit.com/compare. Apple’s wearable offer: https://www.apple.com/uk/watch/. Garmin’s wearable offer: https://buy.garmin.com/en-GB/GB/c10002-p1.html? sorter¼featuredProducts-desc. Samsung’s wearable offer: https://www.samsung.com/uk/wearables/. Cardiosecur – providers’s website: https://www.cardiosecur.com/. AliveCor – provider’s website: https://www.alivecor.com/. EkoHealth – provider’s website: https://www.ekohealth.com/. U.S. National Institutes of Health’s National Library of Medicine (NIH/NLM): https://www.ncbi. nlm.nih.gov/pmc. Vitality Health assessment, provided by AIA Vitality in Australia: https://www.aiavitality.com.au/ vmp-au/know_your_health/vitality_health_review.

Chapter 13

Discussion of Reducing the Risk of Cancer in Life and Health Insurance Maria Węgrzyn

13.1

Introduction

Economic processes and socio-economic changes taking place in the economies of many countries directly affect the emergence of new risks and the shifting of the burden of already incurred risks between the parties to the agreements. The need for constant monitoring of ongoing processes and the need to introduce significant changes is, therefore, natural. The most important, however, is to assess the degree of real impact of the change on risk fluctuations and to see potential new threats. A modern look at the occurring phenomena is to identify ways to limit the fulfilment of risk or to strive to eliminate it completely. Insurance is, in a sense, responsibility for the assumed risk.

13.2

Costs of Oncological Treatment and Insurance Products

The need to consider the changes taking place in the insurance market in recent years particularly applies to products from the group of health insurance including (or not) cancer. The costs of treating cancer are very high, and the disease has clearly increasing trends around the world. It should also be remembered that the increase in costs applies not only to the therapeutic process itself. The literature mentions also indirect costs (Ruszkowski and Leśniowska 2010; HTA Report, INFARMA 2014; EY Report 2013) affecting not only the suffering people (lost productivity, sick leave, financial loss of family income, costs of medicine, etc.) but also their families M. Węgrzyn (*) Wroclaw University of Economics and Business, Wroclaw, Poland e-mail: [email protected] © Springer Nature Switzerland AG 2020 M. Borda et al. (eds.), Life Insurance in Europe, Financial and Monetary Policy Studies 50, https://doi.org/10.1007/978-3-030-49655-5_13

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(costs of care for the ill, costs of informal care), as well as entire social groups. This is obviously a problem not so much of insurers but first of all the governments of states, which should financially strengthen actions aimed at treating cancer and preventing its occurrence. However, due to the dedication of health insurance to entire societies, it is difficult not to respond to such defined problems. The role of insurers is to ensure financial security during a difficult period of illness, in accordance with the terms of the contracts. The higher morbidity in societies and the greater the financial needs arising from this, the more desirable insurance products are becoming on the market. The increasing risk of cancer and the issuing social and economic consequences point to the necessity of insurers paying more attention to this. Careful observation of the behaviour of governments should be a guide in the process of creating good product offers by insurers. The differences in the value of financial resources transferred by governments to oncology care are quite significant. This is not due to a significant difference in disease in individual countries but to the adopted health policies and financial possibilities. Logic dictates that the supply offer of health insurance products, including those related to cancer, will be the highest in countries where the value of funds allocated to the treatment of cancer is the lowest. Therefore, the demand for insurance coverage should be the highest there. However, this is not the case. Poland is a good example here (Table 13.1). The value of financial resources allocated to oncological care in relation to the value of funds allocated in other countries is the lowest (per capita). In the face of imperfect access to oncological services offered in the Polish social insurance system, a market for additional insurance is increasing in the private segment. It has the form of separate private health insurance in case of a cancer diagnosis or is an extension of life insurance with an additional risk. However, insurance products of this nature were offered for purchase in Poland no sooner than in 2015, i.e. much later than in other countries. Such an action is probably due to the poor purchasing capacity of households and thus the inability to create an adequate, financially sufficient risk group assigned to such insurance by insurers. Insurance companies can’t ignore the significance of the huge increase of risk of cancer and its consequences. This is why insurance products including such risk constitute an important element of insurance institutions’ offer portfolio, until the time a way of minimising of stopping it has been found. In the context of financing healthcare services and building an insurance offer, the ratio of expenditure to disease burden is also important. Figure 13.1 illustrates expenditure on oncological care in selected European countries in relation to disease burden (measured using the DALY indicator) for the four main types of cancer in relation to the European average. Here, also, Polish oncology expenses in relation to the cost are significantly lower than the European average. The low level of financing of oncological services results in limited access to services. Consequently, the number of people purchasing health insurance in Poland is increasing. According to the data of the Polish Chamber of Insurance (www.piu. org.pl), the number of individuals insuring themselves the most dynamically has

49,700 27,700 20,700

38,800

75,700 31,300 14,200

40,000

17.7

9.4 11.6 7.5

Expenditure on healthcare as a % of GDP 6.9 9.4

7080

7116 3631 1065

Expenses for healthcare per capita EUR 697 2801

6868

4672 3213 1553

Expenses for healthcare per capita (EUR PPP) 1180 2519

4.7

2.5 4.3 8.0

Expenses for oncology as % of healthcare expensesc 6.0 6.1

333

178 156 85

Expenses for oncology per capita (EUR) 42 171

323

117 138 124

Expenses for oncology per capita (EUR PPP) 70 154

Source: (a) Eurostat data for 2013 or 2012 (in the case of data including purchasing power), (b) OECD data for 2011 (2012 for France and Norway), (c) data for Poland and the Czech Republic for 2011, for USA and Great Britain for 2010, for Norway for 2007, and for France the average of values from various sources in 2009–2013. Based on: Cancer Research UK, Cancer Service: Reverse, Pause or Progress, December 2012, Institute for Fiscal Studies, Public payment and private provision, Nuffield Trust, Maj 2013, R. Luengo-Fernandez et al., Economic burden of cancer across the European Union: a population-based cost analysis, University of Oxford, October 2013, The National Cancer Institute, Cancer Trends Progress Report—2011/2012 Update, NIH, DHHS, Bethesda, MD, August 2012, http://progressreport.cancer.gov, SINTEF, Costs of cancer in the Nordic countries—a comparative study of healthcare costs and public income loss compensation payments related to cancer in the Nordic countries in 2007; Société Française de Radiothérapie Oncologique: Livre blanc de la radiothérapie en France, 2013; INCa (red.), Les cancers en France en 2013. Collection état des lieux et des connaissances, Boulogne-Billancourt Cedex, January 2014 and CNAMTS, Améliorer la qualité du système de santé et maîtriser les dépenses: propositions de l’Assurance Maladie Rapport au ministre chargé de la sécurité Sociale et au parlement sur l’évolution des charges et produits de l’assurance maladie au titre de 2014 (loi du 13 août 2004) pour 2014 and Economic information on health care, Zdravotnická Statistika ČR 2012, www.uzis.cz. After: Healthcare systems in selected countries, EY Report commissioned by the Oncology Foundation 2014

Country Poland Great Britain Norway France Czech Republic USA

GDP per capita (EUR PPPb) 17,100 26,800

GDP per capita EURa 10,100 29,800

Table 13.1 Expenditure on healthcare and cancer care in selected countries

13 Discussion of Reducing the Risk of Cancer in Life and Health Insurance 205

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1 0.8 0.6 0.4

Lung cancer

0.2

breast cancer

0 -0.2 -0.4

Prostate cancer Colorectal cancer

-0.6 -0.8 Fig. 13.1 Expenses for cancer care in relation to disease burden for the four main types of cancer (A negative number means lower expenses in relation to the cost than the European average). Source: Improving the efficiency and stability of oncological care. Recommendations for Poland, Report of the All. Can initiative, March 2017. Prepared on the basis of: Cole et al. Improving Efficiency and Resource Allocation in Future Cancer Care. Office of Health Economics/The Swedish Institute for Health Economics, London 2016

been growing by 58% yearly since 2017. In the case of group insurance, there is an increase of 26% (Report of the Foundation of L. Paga 2019). Such development of the insurance market should satisfy both policyholders and the insurers. However, due to the proven growing incidence of cancer and high mortality, which are factors that generate high payments of benefits arising from signed contracts, and the risks covered by them, it is necessary to create conditions limiting the development of cancer. It is therefore reasonable that insurance companies also participate in this process.

13.3

Prevention as a Prerequisite for Insurance Contracts

The main direction of work should be conducting preventive actions and including them in insurance products. A typical division of prevention according to Caplan’s proposal (Caplan 1964) consists of three ranges: • Primary prevention, which aims to eliminate risk factors for a specific disease • Secondary prevention, whose task is to detect the disease at an early stage • Third-degree prevention, aimed at rehabilitation after curing a specific disease The costs of health service increase in relation to the stage of the disease and the level of prevention, as illustrated in Fig. 13.2. The patient’s treatment costs are directly dependent on the stage of the disease at the time of treatment, and the difference in costs is even several times higher when it comes to the expenditure on cancer diagnosed in the fourth stage of the disease in

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Palliative treatment and terminal care Cancer

Treatment

Cost

(Third line prevention) Diagnosis Screening

Symptoms of precancerous disease (Secondary prevention) Lifestyle modification; chemoprevention; vaccinations No symptoms

Prevention

(Primary prevention) Time/Age

Fig. 13.2 The cost of healthcare depending on the stage of the disease. Source: Own

120 100

80 60

USA

40

Great Britain

20

Poland

0

Fig. 13.3 Mortality trends for breast cancer per 100,000 people, 1959–2008, age 50–69 (Average of coefficients from the age groups 50–54, 60–64, 65–69). Source: World Cancer Report (2008), Edited by Peter Boyle and Bernard Levin, IARC, Lyon 2008

relation to the treatment and thus the costs of diagnosed cancer in situ (pre-invasive) (Zawadzki 2017). At this point it should be emphasised that the main goal of cancer treatment in advanced stages is palliative treatment, which is currently becoming treatment aimed at changing the qualification from palliative disease to chronic disease. Therefore, the answer to limiting the increase in the cost of oncological health services will be to stop the risk at the level of primary and secondary prevention through, e.g. mandatory participation in cyclic screening tests. The benefit obtained would not only mean lower financial costs but also, no less important, lower socioeconomic costs. Figure 13.3 presents trends in mortality from breast cancer in the

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EU28 Poland Sweden Germany Spain France Austria

Computer tomograph Magnetic resonance imaging 0

10

20

30

40

Fig. 13.4 Number of medical devices per one million people in 2014. Source: Own, based on Health at a Glance: Europe 2016. STATE OF HEALTH IN THE EU CYCLE, OECD

USA, Great Britain, and Poland and their changes due to the introduction of primary and secondary prevention. The diagnosis of cancer consists of tests performed to determine the presence of the tumour, to characterise its histological structure, and to determine its stage and differentiation. At the stage of diagnosing cancer, the most important are imaging tests, such as ultrasound (USG), X-ray, and computed tomography (CT), as well as endoscopy, mammography, scintigraphy, and angiography. In most cases, there is no need for state-of-the-art tests, such as PET-CT, or magnetic resonance imaging (MRI), and their role is rather to resolve doubtful cases or to thoroughly assess disease progression in a specialised oncology centre. However, the ability to conduct research is conditioned by having diagnostic equipment and adequate accessibility to it. The level of access to diagnostic services in the field of oncological services in Poland is unsatisfactory in relation to the availability shown by other countries. The number of CT scanners in Poland in 2014 was clearly lower than the number of these scanners in other countries and lower than the average among 28 EU countries. In the case of magnetic resonance imaging devices, the differences were less visible, but also significant, as shown in Fig. 13.4. Analysing the number of tests carried out using the equipment owned, the situation is similar. A small number of equipment in Poland does not allow for large-scale tests. It is also worth noting that in 2016 the number of both MRI (magnetic resonance imaging) and CT (computed tomography) tests in Poland decreased compared to 2014. This is probably the result of the research financing method adopted by the public payer (Figs. 13.5 and 13.6). These compilations show that strengthening the resource potential in Poland by increasing the number of diagnostic equipment necessary to perform screening is an absolute necessity. This is a clear gap that insurance companies can fill to obtain not only a reduction in the cost of benefits arising from the implementation of health insurance contracts but also a high assessment of satisfaction with insurance products held by potential patients. The introduction of screening and diagnostic tests into the practice of insurance companies in Poland would be an important step towards limiting the risk accepted for protection.

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EU25 Poland

2016

Spain

2014

France 0

20

40

60

80

100

120

Fig. 13.5 Number of MRI tests per 1000 people. Source: own study based on Health and healthcare in 2017, Statistical analyses of the Central Statistical Office and Health at a Glance: Europe 2016. STATE OF HEALTH IN THE EU CYCLE, OECD

EU 25 Poland

2016

Spain

2014

France 0

50

100

150

200

250

Fig. 13.6 Number of computed tomography tests per 1000 people. Source: own study based on Health and healthcare in 2017 Health and healthcare in 2017, Statistical analyses of the Central Statistical Office and Health at a Glance: Europe 2016. STATE OF HEALTH IN THE EU CYCLE, OECD

The availability of medical services for patients in the field of oncology is also determined, among others, through the geographical distribution of service providers, as well as the population density and geographical conditions of the country (OECD Report 2016; Sowada et al. 2019; Ambroggi et al. 2015). In smaller countries, there is a tendency to centralise oncology centres. An example would be the Czech Republic, where there are 13 comprehensive oncology centres (CCC) located in major cities. In large countries, on the other hand, ensuring adequate access of patients from various areas to oncological services requires greater decentralisation of these services, in particular in the case of chemo- and radiotherapy. In England, under the NHS structure, radiotherapy diagnostics and treatment are provided by 50 NHS trusts located in 58 hospitals. The statutory maximum travel time for patients for radiation therapy is also specified: 45 min. There are 21 cancer centres in France, a network of rare cancer treatment centres, and around 90 centres practising all three types of therapy (radiation, chemotherapy, and surgery). On the other hand, in the USA, there are 1500 oncological treatment entities, which means that the availability of oncological services varies greatly geographically. There are also no national standards for patients’ travel to healthcare providers. A specific case is Norway, where there are only six specialised university oncology centres. However, it is a country with an extremely low population density and a large geographical spread (EY Report 2014). In Poland, however, the availability of oncological service providers is described in Maps of Health Needs (mpz.mz.gov.pl).

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From the point of view of insurance companies in terms of potential places for performing diagnostic or restorative tests in Poland, it should be recognised that we are dealing with a very large dispersion and fragmentation of service providers. This, in turn, causes measurable difficulties in choosing medical entities cooperating with insurance companies and in accessing medical services. Private health insurance, including that related to cancer, in addition to introducing the necessity of screening and preventive measures, could also include reimbursement of funds allocated to specialistic non-refundable medicines under general health insurance. Here, it should be remembered that the cost of therapy in some cases may exceed the sum of the insurance. However, as research shows (ALIVA Report 2017), access to innovative drug therapies contributes to shortening treatment time, which can give measurable results in the process of liquidation of claims under insurance contracts.

13.4

The Offer of Cancer Cover Health Insurance, the So-called “Onkopolisa” (Oncopolicy), and Limitations on Its Attractiveness in Poland

Insurance companies offering additional insurance against cancer are defining the scope of insurance covering services directly related to the occurrence of cancer. Benefits are obtained only at the time of confirmed medical diagnosis. Analysing the general terms of cancer cover insurance of selected insurers (Nationale-Nederlanden, AXA) operating on the Polish market, one can identify potential anti-stimuli of the process of more commonly strengthening the significance of “oncopolicy” in real space. As a result of an in-depth analysis of the general insurance conditions (GTC), elements that limit the attractiveness of additional cancer cover insurance can be identified. These are primarily: 1. Method of obtaining the right to a benefit: medical diagnosis supported by histopathological examination. 2. Period of insurance coverage: usually, the full protection guarantee is granted after a specified period (90 or 180 days) from the date of commencement of insurance coverage. 3. Staged benefit payment system used by the majority of insurers, e.g. in the case of diagnosing cancer: 30–40% of the insurance sum, in the case of surgery: 20–30% of the insurance sum, in the case of chemotherapy cycle: 1.5–3% of the insurance sum. 4. Outpatient benefits provided only in the institutions selected by the insurer. 5. Applicable exclusions. Analysing the assumptions of “oncopolicies”, it should be noted that the basis for obtaining the benefit is the confirmation of the diagnosis by histopathological examination. To obtain this type of result, in most cases it is usually necessary to

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perform surgery. However, less invasive ways to diagnose cancer, such as fineneedle biopsy with flow cytometry in the case of lymphoma, are not included. Often, the very process of recognition requires the involvement of large private financial resources in order to obtain the most precise and reliable view of the situation. The phased rigid payment of benefits also seems a rather inflexible solution. The costs of chemotherapy differ significantly depending on the type and stage of cancer and the treatment strategy adopted. In many cases, 3% of the sum insured may be insufficient security for benefits. An example would be the use of the cytostatic nivolumab in lung cancer therapy, where the monthly cost of treatment ranges between 20 and 25 thousand PLN. Another limitation resulting from additional insurance against cancer is the need to perform outpatient services only in selected medical facilities indicated by the insurance company. The need to adapt to the insurer’s guidelines in this respect may cause discomfort to the patient. A significant distance from the treatment centre (geographical dispersion) or the place of outpatient services for cancer patients during therapy or convalescence negatively affect their mental and physical condition as well as the speed of diagnosis or therapy. This is particularly important in the case of patients who, due to the specificity of the treatment, must resort to welldefined outpatient services at regular short intervals (e.g. once a week). The nuisance of travel (identified here as the distance of the outpatient unit from the place of residence or travel time) can have a negative impact on the effects of the treatment process. This factor also indirectly affects the quality of life. The possibility of any choice (or selection from a large number of proposals) of units providing outpatient services allows, however, to maximise the benefits of the adopted treatment regimen. Insurance companies also apply certain exemptions for the types of cancer covered. If they are diagnosed, no funds are available for treatment. From the perspective of the potential policyholder, in the case of a specialised package (protection against cancer), the use of exclusions regarding the type of disease pathologies may prove to be an element affecting the low attractiveness of insurance. Very often, certain types of cancers are in situ excluded. Considering, however, that from a medical point of view, diagnosing the disease and the use of rapid therapy at this stage minimises the risk of invasive pathological conditions, the possibility of obtaining funds supporting decision in this regard seems to be very important. Given the above-mentioned design limitations of additional cancer insurance, a kind of cognitive dissonance between what is expected and what is offered may be characteristic. This insurance may be a moderate supplement to the financing of ongoing therapies. However, due to the form of the structure, it does not offer broad innovative possibilities in the field of diagnostics and treatment. The limitations in the structure of the attractiveness of health insurance policy offers including cancer diseases presented in Table 13.2 directly affect the lower than possible level of sales and unsatisfactory level of satisfaction of the insured. And although this market in Poland is developing very dynamically, its success should not be seen in the attractiveness of products, but in the weakness of the Polish healthcare system and, in large, difficulties in the availability of services. Strong reinforcement of cancer cover offers should be expected after the introduction of the

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Table 13.2 Selected elements of general conditions of insurance in case of cancer GTC elements Types of cancer covered

Insurer AXA All malignant neoplasms (C00-C97 according to ICD10), neoplasms in situ (D00-D09 according to ICD10).

Exceptions

• Malignant melanoma (C43 according to ICD10) of 1 A or less • Malignant skin neoplasms (C44 according to ICD10) • Thyroid malignancies less than 2 cm in diameter • Chronic A lymphocytic leukaemia • Malignant prostate tumour below 6 points on the Gleason scale • Malignant bladder cancer at Tis and Ta stage • In situ cancer: skin, thyroid, bladder • Any precancerous changes The scope of insurance does not include cancers that have been diagnosed, operated on within the first 3 months of insurance coverage

Insurance period

Obtaining the right to a benefit Payment method

Medical diagnosis supported by histopathological examination The benefit is paid in stages: • Diagnosis: 40% of the insurance sum (malignant or benign brain cancer) or 20% of the insurance sum (cancer in situ) • Surgery: 30% of the insurance sum (malignant or benign brain cancer) or 15% of the insurance sum (cancer in situ) • Chemotherapy course: 3% of the insurance sum (malignant or benign brain cancer) or 1.5% for in situ cancer • Radiotherapy course: 3% of the insurance sum (malignant or benign brain cancer) or 1.5% for in situ cancer

ING In the “she” and “he” variants, all malignant neoplasms (C00-C97 according to ICD10), benign brain tumour, benign thyroid tumour, benign ovarian cancer, pre-invasive cancer: ovary, breast, endometrium, fallopian tube, testicular • Cancers coexisting with HIV infection • Any skin cancer (code C44 according to ICD10) • All pre-invasive cancers not indicated in the GTC

In case of illness supported by a diagnosis within 90 days from the date of commencement of liability, only payment of the benefit in the amount equal to the sum of contributions paid up to the day of diagnosis occurs Medical diagnosis supported by histopathological examination The benefit is paid in stages: • Diagnosis: 40% of the insurance sum • Operation: 40% of the insurance sum • Chemotherapy course: 5% of the insurance sum for each • Radiotherapy course: 5% of the insurance sum for each • Recovery: 5% of the insurance sum

Source: own study based on: AXA general insurance conditions cancer assistance (full option), https://axa.pl/pomoc-na-raka/ (2017); general terms and conditions of Nationale-Nederlanden cancer insurance (options “He” and “She”), https://www.nn.pl/rakowi-wspak (2017)

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necessary conditions related to preventive measures as well as the financing or co-financing of modern, innovative treatment methods and entire drug therapies.

13.5

Summary and Conclusions

A dynamically growing number of cancer cases and an exceptionally high mortality rate recorded in Poland in relation to the incidence and mortality rate in other countries affect the need to purchase additional insurance. Products offered on the Polish market, as well as their design, do not always respond to the identified needs of buyers. Considering the organisational specificity of the healthcare system in Poland, with particular emphasis on the area of oncological treatment, it can be presumed that, in its current form, the market of additional insurance in the event of cancer will show a downward or stagnant tendency. The possibility of in-depth diagnostics and a wide spectrum of treatment is strongly limited due to the low availability of specialised diagnostic equipment. “Oncopolicies” provide services (e.g. outpatient) as standard, and additional funds from insurance are often largely insufficient. Due to the fact that the costs of implementing contracts under cancer cover insurance are relatively high, which is due to high medical costs, i.e. the costs of the treatment process and the so-called indirect costs, it is necessary to introduce permanent elements of constant prevention of cancer into insurance products. The attractiveness of the offer should be complemented by enabling financing or co-financing of modern, innovative treatment methods and entire drug therapies. Such action will increase the attractiveness of insurance products and increase the number of their buyers in the same way as it happens in numerous countries of the world.

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