Public

Issues Paper on the Use of Big Data
Analytics in Insurance
February 2020

Issues Paper on the Use of Big Data Analytics in Insurance
Adopted by the Executive Committee on 26 February 2020

Page 1 of 41

About the IAISThe International Association of Insurance Supervisors (IAIS) is a voluntary membershiporganisation of insurance supervisors and regulators from more than 200 jurisdictions. The
mission of the IAIS is to promote effective and globally consistent supervision of the insurance
industry in order to develop and maintain fair, safe and stable insurance markets for the benefit
and protection of policyholders and to contribute to global financial stability.
Established in 1994, the IAIS is the international standard setting body responsible fordeveloping principles, standards and other supporting material for the supervision of theinsurance sector and assisting in their implementation. The IAIS also provides a forum forMembers to share their experiences and understanding of insurance supervision andinsurance markets.
The IAIS coordinates its work with other international financial policymakers and associationsof supervisors or regulators, and assists in shaping financial systems globally. In particular,
the IAIS is a member of the Financial Stability Board (FSB), member of the Standards Advisory
Council of the International Accounting Standards Board (IASB), and partner in the Access to
Insurance Initiative (A2ii). In recognition of its collective expertise, the IAIS also is routinely
called upon by the G20 leaders and other international standard setting bodies for input on
insurance issues as well as on issues related to the regulation and supervision of the global
financial sector.
Issue Papers provide background on particular topics, describe current practices, actual
examples or case studies pertaining to a particular topic and/or identify related regulatory andsupervisory issues and challenges. Issues Papers are primarily descriptive and not meant tocreate expectations on how supervisors should implement supervisory material. Issues
Papers often form part of the preparatory work for developing standards and may containrecommendations for future work by the IAIS.
International Association of Insurance Supervisors
c/o Bank for International Settlements
CH-4002 Basel
Switzerland
Tel: +41 61 280 8090 Fax: +41 61 280 9151
www.iaisweb.org
This document was prepared by the Market Conduct Working Group in consultation with IAIS
Members.
This document is available on the IAIS website (www.iaisweb.org).
© International Association of Insurance Supervisors (IAIS), 2020.
All rights reserved. Brief excerpts may be reproduced or translated provided the source isstated.

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Contents
Executive summary ............................................................................................................... 4
Acronyms .............................................................................................................................. 61

2

3

Introduction .................................................................................................................... 7
1.1

Background and purpose ........................................................................................ 7

1.2

Approach and structure ........................................................................................... 8

Types, sources and processing of data in insurance .................................................... 10
2.1Type and sources of data in insurance.................................................................. 10

2.2

Processing of data in insurance ............................................................................ 11

The use of big data analytics across the insurance product lifecycle ............................ 14
3.1

3.1.1

Personalised insurance cover ........................................................................ 14

3.1.2

Targeted marketing ........................................................................................ 16

3.1.3

Sales execution ............................................................................................. 17

3.1.4

Distribution and advice ................................................................................... 18

3.2

Pricing and underwriting........................................................................................ 19

3.2.1

Increased granularity in risk selection and pricing .......................................... 19

3.2.2

Price optimisation .......................................................................................... 20

3.2.3

Underwriting .................................................................................................. 22

3.3

4

Product design, marketing, sales and distribution ................................................. 14

Claims handling .................................................................................................... 25

3.3.1

Risk mitigation and loss reduction .................................................................. 25

3.3.2

Claims processing ......................................................................................... 26

3.3.3

Fraud detection .............................................................................................. 28

Supervisory considerations .......................................................................................... 31
4.1

Suitability, affordability and availability of insurance cover .................................... 32

4.2

Governance and oversight of algorithms ............................................................... 34

4.3

Third party risk management ................................................................................ 38

4.4

Issues relating to privacy, data protection and ownership of data.......................... 38

4.5

Other broader considerations ................................................................................ 39
Some examples of traditional and non-traditional data sources and data types ininsurance ....................................................................................................... 40
Reference material on impact of BigTechs, FinTechs and third party cloudservice providers ............................................................................................ 41

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Executive summary
1.
This paper builds on the November 2018 IAIS Issues Paper on Increasing Digitalisationin Insurance and its Potential Impact on Consumer Outcomes 1 (Digitalisation Paper) by
focusing more specifically on issues relating to the use of personal and other data by insurers
as a result of digitalisation.
2.
The scope of this paper focuses on the use of algorithms and advanced analytics
capabilities by insurers to make decisions based on patterns, trends and linkages and theavailability to insurers of new data sources, collectively referred to as “big data analytics”(BDA).
3.
As mentioned in the Digitalisation Paper, supervisors will need to consider how tobalance the many benefits of technological innovation to the insurance sector with potential
risks of poor outcomes to customers as a result of such innovation. To help understand thepotential benefits and risks of the use of BDA by insurers, this paper considers the new ways
in which insurers are able to collect, process and use data across various stages of theinsurance product lifecycle, namely product design, marketing, sales and distribution, pricing
and underwriting and claims handling. This paper also highlights potential considerations forsupervisors relating to the use of BDA in insurance to ensure the fair treatment of customers
as described in Insurance Core Principles (ICPs) 18 and 19.
4.
The insurance sector has always been heavily reliant on data for accurate risk
assessment, underwriting and pricing. The increasing availability of data and enhanced
processing capabilities now accessible to insurers can result in a number of benefits for both
insurers and customers. The granularity of data from multiple sources can lead to more
personalised and affordable insurance products, increased choices and more efficient
servicing for customers. Insurers can also benefit from BDA by expanding their distribution
reach, ensuring more accurate pricing and lowering their cost margins due to improved
operational efficiencies, better fraud detection and fewer claims as a result of improved risk
behaviour by customers.
5.
On the other hand, the complexity and opacity of algorithm technology and the abilityof insurers to customise product offerings to an individual level could potentially result in risks
to individual customers, as well as to the insurance sector as a whole. Supervisors may needto devise appropriate and proportionate responses to deal with such risks, suited to theirrespective jurisdictional frameworks and mandates. In particular supervisors may wish toconsider whether the use of BDA could adversely impact the availability and affordability of
insurance, potentially resulting in reduced options or no coverage for certain consumer
segments. Additionally, they may find it useful to explore the need to enhance governance,oversight and third party risk management requirements specific to the use of algorithms for
BDA purposes. This could include clarifying supervisory expectations on appropriate levels oftransparency and insurer accountability for customer outcomes resulting from algorithm-based
decisions.
6.
While the collection, use and processing of data naturally raises issues about privacy,
data protection and related matters, the supervision of such matters often falls outside theremit of insurance supervisors, and is not covered in detail within the scope of this paper
1https://www.iaisweb.org/page/supervisory-material/issues-papers//file/77816/issues-paper-on-

increasing-digitalisation-in-insurance-and-its-potential-impact-on-consumer-outcomes .
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except to the extent addressed in ICP 19.12 2. Nevertheless, insurance supervisors may needto engage with various cross-sectoral stakeholders on potential consumer privacy and dataprotection risks associated with the use of BDA in insurance, most notably the relevant dataprotection agencies in their respective jurisdictions.

2 ICP 19.12 states that “[t]he supervisor requires insurers andintermediaries to have policies andprocedures for the protection and use of information on customers”.

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Acronyms
A2ii

Access to Insurance Initiative

ABI

Association of British Insurers

AIDA

Artificial Intelligence and Data Analytics

APIs

Application Programming Interfaces

ACCC

Australian Competition and Consumer Commission

AFM

Autoriteit Financiële Markten (Netherlands)

AI

Artificial Intelligence

BaFin

Bundesanstalt für Finanzdienstleistungsaufsicht (Germany)

BDA

Big Data Analytics

DNB

De Nederlandsche Bank

EIOPAEuropean Insurance and Occupational Pensions Authority

FACI

Federal Advisory Committee on Insurance (USA)

FCA

Financial Conduct Authority (UK)FEAT

Fairness, Ethics, Accountability and Transparency

FSCA

Financial Sector Conduct Authority (South Africa)

G20Group of Twenty

GIA

General Insurance Association (Singapore)

GINA

Genetic Information Nondiscrimination Act of 2008 (USA)HKFI

Hong Kong Federation of Insurers

IAIS

International Association of Insurance Supervisors

ICPs

Insurance Core Principles

IIB

Insurance Information Bureau of India

IoTInternet of Things

IPPC

Insurance and Private Pensions Committee (OECD)

IRDA

Insurance Regulatory and Development Authority (India)

MAS

Monetary Authority of Singapore

NAIC

National Association of Insurance Commissioners (USA)OECD

Organisation for Economic Co-operation and Development

SAICB

South African Insurance Crime Bureau

TCIP

Turkish National Catastrophe Insurance Pool

UBI

Usage Based Insurance

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1Introduction

1.1

Background and purpose

7.
In November 2018 the IAIS published an Issues Paper on Increasing Digitalisation in
Insurance and its Potential Impact on Consumer Outcomes 3 (Digitalisation Paper). That paper
considered generally the impact of various technological innovations on consumer outcomes
and insurance supervision in light of Insurance Core Principles (ICPs) 18 on Intermediaries
and 19 on Conduct of Business.
8.
One of the areas identified in the above paper was the increasing availability of, andaccess to, consumer related-data by insurers as a result of many of the innovations describedin that paper. The IAIS signalled that this would be an area for further exploration anddiscussion in a separate paper.
9.
The purpose of this paper is therefore to consider the use of personal and other databy insurers as a result of digitalisation and to identify potential benefits and risks for consumers
associated with such use. Specifically this paper focuses on the increasing use of algorithms
and advanced data analytics by insurers, as well as the greater availability to insurers of
traditional and non-traditional data sources. In the paper this will be collectively referred to as“big data analytics” (BDA).
10.
Where data is processed and held by intermediaries, as may be the case in certain
circumstances depending on their involvement in the insurance value chain, the issues
identified in this paper will also be applicable as and when appropriate to intermediaries. In
these circumstances references to “insurer” in the paper would include insuranceintermediaries.
11.
In this paper “consumer” refers to the “universe of all actual and potential customers
for insurance products” and “customer” refers to the “[p]olicyholder or prospective policyholder
with whom an insurer or insurance intermediary interacts, and includes, where relevant, otherbeneficiaries and claimants with a legitimate interest in the policy”, as defined in the IAIS
Glossary. 4
12.
This paper does not go into technical detail on the various types of technologicalinnovations 5 now available to insurers, but rather discusses various applications of BDA byinsurers across the insurance product lifecycle and considers the potential impact of theseapplications on outcomes for consumers and possible implications for supervisors as a result.
13.
Customer segmentation on the basis of risk classes is a core feature of private
insurance. Increasing digitalisation enables insurers to access progressively larger volumesof granular customer data. This, together with increasing access to advanced computing andanalytical tools, creates opportunities for greater individualisation of risks and pricing. With
more accurate risk assessments, insurers can offer more appropriate, tailored services and3https://www.iaisweb.org/page/supervisory-material/issues-papers//file/77816/issues-paper-on-

increasing-digitalisation-in-insurance-and-its-potential-impact-on-consumer-outcomes .
4 https://www.iaisweb.org/page/supervisory-material/glossary .
5 Previous IAIS material describing the various types of technological innovations available to insurers:
https://www.iaisweb.org/page/supervisory-material/other-supervisory-papers-andreports//file/65440/report-on-fintech-developments-in-the-insurance-industry;
https://www.iaisweb.org/page/supervisory-material/application-papers//file/77815/application-paperon-the-use-of-digital-technology-in-inclusive-insurance; IAIS Digitalisation Paper .
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products that better match their customers' needs and preferences. More granular customer
segmentation may also provide opportunities for new or incumbent insurers to specialise incertain market segments, resulting in more choice for consumers.
14.
The application of BDA may allow insurers to cluster customers in increasingly refinedrisk categories for different treatment in respect of risk selection, marketing, sales execution,
underwriting, pricing and claims settlement. Used appropriately, BDA has the potential to
improve insurance access for historically underserved consumers, such as individuals withchronic illnesses who may have previously been unable to obtain life insurance cover. 6
However, the use of increasingly refined risk categorisations in some BDA applications could
potentially signal a fundamental shift away from the defining principle of risk pooling for
insurance purposes. This could result in accessibility and affordability challenges and possible
market exclusion for some customers. Additionally, the opacity of algorithms can contribute to
decreased comparability of products, especially if customers are only able to see or accessproduct offerings that are individually tailored.
15.
ICPs 18 and 19 contemplate the delivery of fair customer outcomes across theinsurance value chain irrespective of the adoption of technology or the use of more traditional
means to design, market, sell, distribute or service insurance policies. A thorough and ongoingunderstanding of the benefits and risks associated with the application of BDA in insurance
can help supervisors to develop more appropriate and proportionate responses to suchapplications by insurers while ensuring that the fair treatment of customers is notcompromised.
16.
The application of BDA also raises important questions related to the protection,
ownership, transferability and ethical use of customer data. In many jurisdictions, privacy anddata protection issues generally fall outside the remit of insurance supervisors. Therefore,
while this paper acknowledges the importance of these issues for insurance supervisors, itdoes not cover them in detail except to the extent set out in ICP 19.12, which states that “[t]hesupervisor requires insurers and intermediaries to have policies and procedures for theprotection and use of information on customers”.
1.2

Approach and structure

17.
In line with the approach adopted in the previous Digitalisation Paper, this paper
discusses the applications of BDA across the insurance product lifecycle, and covers productdesign, marketing, sales and distribution, pricing and underwriting, and claims handling.
18.

The paper is structured as follows:•

section 2 provides an overview of traditional and new data sources, data collection
methods and data processing capabilities available to insurers;•

section 3 discusses potential applications of BDA across certain aspects of theinsurance product lifecycle, and the associated benefits and risks of such applications;and

6 http://news.ambest.com/ArticleContent.aspx?pc=1009&altsrc=158&refnum=285595 .

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•section 4 concludes with key considerations, supplementary to those highlighted in theprevious Digitalisation Paper 7, aimed at assisting supervisors to determine possible
responses to the issues discussed in section 3.

7 IAIS Digitalisation Paper at page 24.

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2Types, sources and processing of data in insurance

2.1

Type and sources of data in insurance

19.
The insurance sector has always been heavily reliant on various types of data, used
for many purposes across the insurance product lifecycle, most notably risk selection,marketing, product development and claims management. 8 Traditionally this has included among others - demographic, medical, exposure, behavioural and loss data. This data is
usually sourced directly from customers themselves.
20.
Digitalisation has resulted in the availability and use of new data sources and data sets
for insurers. This type of data is not always collected or produced by the insurer as a result of
direct customer interactions but is also taken from indirect sources such as mobile devices,sensors, satellite technology and the internet, and may be accessible to insurers both within
and outside an insurance context.
21.
A non-exhaustive list of traditional and non-traditional data types and sources ininsurance has been included in Annex 1, for illustrative purposes. Depending on thecircumstances and, as a result of the ongoing increase in data availability, insurers may collectand use other types of data from other sources not specifically listed in Annex 1.
Examples:Netherlands
In the Netherlands, an important source of data is MijnOverheid, where personal data isstored from different institutions, such as government services and pension funds. For
example, customers that ask financial planners to develop financial plans, can download
their pension data from MijnOverheid and then upload it to the server of their financial
planners. This way, all their pension entitlements are immediately taken into account for
their financial plans. Other data in MijnOverheid is an individual’s income data.
Singapore
A key component of Singapore’s National Digital Identity system is MyInfo, a digital service
that enables citizens to authorise third parties to access their personal data that is stored
across different government agencies. With consent, third parties can use MyInfo’s
application programming interfaces (APIs) to access government-verified data toauthenticate their customers and make business decisions relating to them. Currently
more than 20 financial institutions are using MyInfo to provide more than 110 digitalfinancial services.
European Union
The European Insurance and Occupational Pensions Authority (EIOPA) recently
conducted a thematic review on the use of BDA in motor and health insurance. The review
showed how traditional data sources such as demographic data or exposure data are
increasingly combined (not replaced) with new sources like online media data or telematics
data, providing greater granularity and frequency of information about consumers’
characteristics, behaviour and lifestyles. 9 This enables the development of more tailoredproducts and services and more accurate risk assessments.

8 Risk selection includes pricing and underwriting; marketing includes distribution and sales activities;

and claims management includes fraud detection.
9
https://eiopa.europa.eu/Publications/EIOPA_BigDataAnalytics_ThematicReview_April2019.pdf .
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2.2

Processing of data in insurance

22.
The processing of customer data, whether done in-house or by an outsourced third
party provider, is a critical component of an insurer’s business. It helps inform, for example,
the insurer’s business and marketing strategies, service delivery and distribution models,
product range, risk selection and pricing structures.
23.
Digitalisation has resulted in significant changes to the manner in which data can beprocessed and leveraged by insurers as they have access to greatly enhanced levels ofcomputing power and analytical capabilities.
24.
The increasing availability of customer data, more sophisticated tools for collecting,safeguarding, transferring and analysing data, and growing appetite for the use of newtechnologies creates increased opportunities for insurers to adopt the use of algorithms andadvanced data analytics across the insurance product lifecycle.
25.
Algorithms can be used by insurers for direct customer servicing (eg to provide
automated advice and pre- and post-sales support, and improved claims handling) or moreindirectly (eg to design targeted advertising campaigns, obtain insights on consumer
preferences, influence consumer behaviour, inform product design, risk selection and pricing,
determine credit history, conduct “know your customer” and other identification and verification
checks).
26.
The increasing reliance on the use of algorithms by insurers for the processing ofcustomer data creates a number of potential challenges and risks.
27.
Algorithms can be complex and are often treated as proprietary and highly confidential
in nature. Thus, there can be a lack of transparency and strong asymmetry of understanding
between those who design and use algorithms and customers and supervisors seeking tounderstand the outcomes generated by these algorithms. Clear documentation regarding themanner in which algorithms are intended to operate within the insurers’ various businessprocesses may assist to ensure improved transparency and understanding in this regard. 10
28.
The effectiveness of algorithms is dependent on the quality, accuracy andcompleteness of available data, and can be hampered by possible errors in its initial designor programming.
29.
Some algorithms are based on machine learning, which means that as the algorithm
collects and analyses more data, it is able to modify itself without human intervention after
initiation. Algorithms based on machine learning may raise questions about the accountability
and transparency of the decision making process as well as explainability of outcomes.
Enhancements in machine learning capabilities may also result in the operation of algorithms
becoming increasingly complex even for those who design them.
30.
Additionally, machine learning algorithms are based on historical data and, therefore,
generally reproduce the past. This can increase the likelihood that algorithms may perpetuate
unforeseen biases, which in turn create risks of errors potentially resulting in inequitable or
10 See ICP 8.2 which states that “[t]he supervisor requires the insurer to establish, and operate within, an
effective and documented system of internal controls”. According to ICP 8.2.4, “[a]n effective internal controls
system typically includes [. . . ] a centralised documented inventory of insurer-wide key processes and policies
and of the controls in place in respect of such processes and policies, that also may introduce a hierarchy
among the policies[.]”.

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unsuitable customer outcomes. While the potential for biased decision making has always
existed in insurance and is not unique to algorithms, the scale and speed at which such biases
could be perpetuated through self-learning algorithms may merit particular attention by
insurers and supervisors.
31.
There is also a risk that customer segments could be differentiated on the basis offalse assumptions or false conclusions drawn by algorithms on the basis of theseassumptions, resulting in unlawful discrimination against certain customers. 11 In such cases,
it would be important that the underlying assumptions or conclusions causing suchdifferentiation are properly understood, and appropriate remedial measures are implemented
where necessary.
32.
Algorithms may be used alongside, rather than instead of, established techniques suchas generalised linear models, to provide new insights to insurers. Nevertheless thegovernance and oversight of algorithm applications may prove to be challenging and could
require specific technical expertise and more robust auditing mechanisms within insurers toadequately mitigate risks that could potentially arise as a result of the above factors.
Examples:Netherlands
The Netherlands Autoriteit Financiële Markten (AFM) has published guidance on thedevelopment of algorithms used in robo advice. A key requirement is testing of thealgorithm, both before and after launching the robo advisor. When developing the robo
advice, the insurer must be satisfied that sufficient knowledge of financial products ispresent in order to ensure that the robo advice is of sufficient quality. The development
and testing of the robo advice must be carried out thoroughly enough so that it can bedemonstrated that the advice is suitable when the system goes live.
The supervisor’s approach to supervising robo advice and its underlying algorithms is asfollows:(i) the supervisor will assess the input and output of the robo advisor. This is equivalent tothe supervision of physical advice files where consideration is given to whether all relevant
data has been obtained and used in order to arrive at the appropriate outcome based on
the customer’s needs and circumstances;(ii) the supervisor will assess the conditions under which the algorithm was developed inorder to determine whether it was developed with enough knowledge of the advice process
and adequately tested; and(iii) the supervisor is now also considering whether it should directly supervise the designof the algorithm itself.
Singapore
In its Guidelines on Provision of Digital Advisory Services, the Monetary Authority ofSingapore (MAS) requires digital advisers to put in place adequate governance andsupervisory arrangements to effectively mitigate the risks that stem from the use ofalgorithms. 12 These include requiring:

11 https://www.bafin.de/SharedDocs/Downloads/EN/dl_bdai_studie_en.html at page 177.

12https://www.mas.gov.sg/-/media/MAS/Regulations-and-Financial-Stability/Regulations-Guidance-

and-Licensing/Securities-Futures-and-Fund-Management/Guidelines-on-Provision-of-Digital-AdvisoryServices--CMGG02.pdf .
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(i) board and senior management of digital advisers to maintain effective oversight andensure that there are sufficient resources to monitor and supervise the performance of thealgorithms; and(ii) digital advisers to have in place policies and procedures governing the development,
monitoring and testing of algorithms, as well as when changes are made to the algorithms.

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3

The use of big data analytics across the insurance product lifecycle

33.
BDA can be used across various components of the insurance product lifecycle, as
shown by the examples in diagram 1 below.
34.
This paper does not cover all the elements contained in diagram 1, but rather focuses
on a few key areas of the lifecycle where the use of BDA has been identified as potentiallyintroducing significant benefits and risks for insurers, consumers and supervisors.
35.
Some aspects such as robo advice 13 and price comparison websites 14 that werecovered in the previous Digitalisation paper have not been included in this paper. In addition,
the customer interaction component is not discussed separately as represented in the
diagram, but rather incorporated within discussions on the other elements of the productlifecycle where relevant.
Diagram 1: The use of BDA across the insurance product lifecycle
•
•
•
•

•

Personalisation of coverCustomer-specific “targeted”
marketingInternet sales and pricecomparison websites
Social media andsmartphone/device channels
for direct distribution
Robo advice

Product Design,
Marketing,Sales andDistribution

•

•

•
•

3.1

•

•

•

Pricing andUnderwriting

Automated (including nonhuman) product service
centres using robo advice,chatbots and AI
BDA enabling ability to predict
customers’ wants and needs
before they ask
Continuous real-time
customer communication andunderwriting

Product
Management

Telematics data helps customers andinsurers to understand and manage risks(wearables, IoT, smartphones, apps)
BDA enables more effective verification
checks, granular and accurate pricing andfaster underwriting
Granular, customer-specific productofferings (eg usage based insurance)
Genetic data – potential impact on pricing
and availability

Claims Handling

•
•
•

•

•
•
•

•

Platform business models
360 degree view of customer
Continuous real time data
enabling focus on high value
customers
Unstructured data (eg voice)analysis and learning

CustomerInteractions

Fraud detection using BDA
AI and drones in assessing claims
Claims cost efficiencies from AI/automatedassessing, optimised pay-outs, reduced
labour costs
BDA creates new opportunities for risk
mitigation/loss reduction partnerships
between insurers and customers

Product design, marketing, sales and distribution

3.1.1 Personalised insurance cover
36.
Generally there are a number of benefits in using BDA for product design purposes.
Greater access to, and increased efficiencies in the collection and evaluation of, more datatypes can make (new) risks insurable and allow consumers to get cover previously notavailable to them when needed. The use of BDA allows insurers to better understandcustomers’ needs, wants and behaviours, resulting in risk profiling becoming more granular
13 IAIS Digitalisation Paper at page 17.
14 IAIS Digitalisation Paper at page 19.

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and customised. This can enable the design of more personalised products that meetcustomer needs by tailoring the scope and amount of coverage and by setting premium levels
according to individual preferences.
37.
Access to large data sets can enable insurers to design customer specific products
more cost effectively, for the benefit of individual customers or mass customer segments. The
use of BDA can assist specialist intermediaries and underwriters to design increased numbersof customised products with greater efficiency and speed, potentially resulting in wider
customer access and reduced underwriting costs. Mass customisation involves
products/bundles that are designed for mass markets, but which are adjusted slightly for
different customers based on individual customer insights obtained through the application of
BDA. At their core, these products are not truly individualised, in that they simply follow a mass
targeted design template, with minor refinements being made based on the preferences ofparticular customers.
38.
The application of BDA can enable the provision of usage-based insurance (UBI)
products 15 and “on-demand” cover, potentially resulting in more options and improved benefits
for customers. In motor vehicle insurance, UBI may be preferable and more affordable fordrivers who only use their cars occasionally as insurance is charged according to the distancetravelled. UBI products may also have the potential to improve driver safety by providing
immediate feedback to customers on, among other things, harsh braking, dangerous
manoeuvres and excessive speed. Customers may also benefit from premium reductions as
a result of improved driving habits in response to such feedback. In such cases customers
should be assured that any tracking or monitoring of their driving behaviour is done with theirfull knowledge and consent, and without undue limitations on their coverage options.
Example:Germany
In Germany, a number of insurers offer “telematics tariffs” for their motor vehicle policies.
Premiums on telematics tariffs are calculated taking into account the individual driving
behaviour (eg braking, speed and acceleration) and other external factors such as time andplace of the ride. Sometimes there is a maximum age limit for the policyholder. The data isrecorded either via apps or "black boxes" permanently installed in the vehicles. The data
evaluation (ie scoring) is usually not carried out by the insurer itself, but is outsourced tothird parties.
39.
“On-demand” cover, where customers purchase cover for only short periods of time
when they require it, may also be useful for cover during periods of travel or when engaging
in certain higher-risk sporting and recreational activities. This could make insurance cheaperand make customers more conscious of the need to avoid or mitigate risky behaviour.
However, such products could create risks if customers are not periodically reminded toactivate or deactivate their cover as necessary. This could also increase the possibility of fraud
if customers activate their cover only after a risk has materialised.
Example:Japan

15 IAIS Digitalisation Paper at page 11.

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A Japanese insurer launched a “one time” insurance product jointly with a mobile
communications company for certain periodic lifestyle and leisure activities such as golfing
and mountain climbing. For example, users get a text message with a productrecommendation the first time they arrive at a golf course offering insurance that will cover
the expenses of treatment for any injuries and damage liabilities, and asking if they want totake this cover out immediately. The product’s design is based on users’ golf playing
characteristics and allows them to be insured on a daily basis.
40.
It remains the responsibility of insurers to ensure that design decisions are madetransparently and can be clearly explained to customers and supervisors. 16 It is also importantfor insurers to manage the risk of customers potentially misconstruing personalised productofferings as the provision of financial advice in the absence of any necessary suitability andneeds analyses being conducted.
3.1.2 Targeted marketing
41.
Insurers can use behavioural and lifestyle indicators derived from BDA to identify
opportunities that are specific to certain customer segments and to nudge these customers
using targeted marketing messages and campaigns in an appropriate and non-intrusive
manner. For example, couples expecting children can be identified to receive life insurance
messages. These messages can be delivered in real time via smart devices when a customer
is passing an insurer’s branch office or visiting specific websites or other social media
platforms.
42.
Insurers may be able to anticipate risks and the need for an insurance product basedon a customer’s day to day activities. Insurers can then interact with customers in real time tooffer them a specific product or product options to fulfil that need. For example, through theuse of BDA on social media updates and location tracking data an insurer can identify if acustomer is planning a holiday or other type of trip and then, in a targeted manner, offer thecustomer travel insurance. In this way product offerings can be personalised and targeted toa specific customer.
43.
In general, insights gained from BDA present opportunities for insurers to identify
potential gaps in customers’ insurance needs and pre-emptively nudge them to consider
seeking out appropriate cover through the use of targeted advertising. Additionally, this canenable insurers to become more effective partners with their customers to help themunderstand and mitigate their risks. BDA insights can also be used to identify possible
protection gaps and target products at particular customer groups that have been identified ashaving shared characteristics, potentially expanding insurers’ reach to certain underserved
population segments.
Examples:USA
A US-based insurer gathers data from social media platforms as well as its own interactive
digital services and uses sophisticated analytics systems to highly personalise all aspectsof its engagement with customers. Its digital car buying service advises customers not only
when they are buying motor vehicle insurance but also when they are looking to purchase,
repair or sell a vehicle. Moreover, the insurer helps customers plan and manage theirfinances by providing them with fully-digital mobile tools tailored for various stages of life
16 https://www.bafin.de/SharedDocs/Downloads/EN/dl_bdai_studie_en.html at page 13.

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and cultivates close ties with its customers by promoting social media community groups. It
also has an online advisory service which addresses a wide range of professional andpersonal circumstances.
United Kingdom
Customers with special insurance needs, such as cover for pets or expensive gadgets, often
find it difficult to find the right policy or attractive rates. Advanced analytics tools allow
insurers to find and service such customers. A UK-based start-up analyses search engine
and social media data to identify groups of customers with uncommon insurance
requirements. It then approaches insurers on behalf of the group, in order to negotiate better
rates for them.
44.
While digitalisation has enabled opportunities for greater product differentiation andpotentially more choices for consumers, there are also potential risks that tailored or targeted
campaigns may limit the ability of consumers to compare this wider range of product offerings.
This could result in less informed decision-making, reduced choice, greater difficulties in
product switching and potentially loss of coverage for some customers.
45.
Furthermore, targeted campaigns based on BDA insights that inadvertently perpetuatehistoric biases could potentially prolong the exclusion of certain consumer groups from somesegments of the market, specifically in situations where there is a lack of robust oversightmechanisms within an insurer.
46.
Targeted product offerings based on the use of BDA in response to specific customer
behaviours, incidents or activities could also increase the risk of over-insurance as customers
may be influenced to buy more insurance than they need, and/or make decisions aboutpurchasing insurance cover within a specific context without considering their overall financial
needs or affordability. This may be of particular concern in areas with lower levels of financial
literacy, or where there are limited opportunities for customers to interact directly with insurers
and intermediaries and ask questions before purchasing a product.
3.1.3

Sales execution

47.
People may sometimes be unwilling or unable to devote time and energy to makedecisions or go through lengthy administrative processes relating to their insurance. Thesebehavioural biases may discourage customers from making decisions in their interest, suchas switching to other providers or products that are more appropriate for their needs. BDA
insights that reveal these types of customer biases should not be used to design sales-related
processes that could nudge customers to make decisions against their interests.
48.
Instead, insurers can use improved insights on customer behaviour from BDA to help
design less cumbersome sales processes that overcome certain biases and possible inertia.
It is possible, for instance, to use data to pre-populate forms or find other ways to minimise
friction for customers in the sales process. Insurers quoting for home insurance, for example,
could look at data from flood maps rather than asking customers how far they are from water
sources. Shorter, simpler application forms can also make it easier for customers to engage
and reduce the risk of them making mistakes in their answers, which could later be used as abasis to repudiate their claims.
Examples:United Kingdom
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A large UK insurer quoting for home insurance aims to provide accurate premiums using
data customers have previously provided as well as other sources, such as land registrydata, to simplify the quotation process, remove uncertainty for customers and avoid
duplication. In this way, the insurer applies a shortened home insurance process to all homeinsurance ‘quote and buy journeys’ through the insurer’s website, online portal and mobile
app. The insurer intends to apply this to claims processes, arguing that the provision of
accurate data reduces the risk of a having home insurance claims rejected.
CanadaA Quebec-based digital insurance brokerage sources alternative motor vehicle and homeinsurance options for customers with existing cover. They build a detailed profile on behalfof customers and, before their policies expire, present them with a small number of curated
alternatives that can be purchased in minutes. Powered by artificial intelligence and licensed
insurance brokers, it shops around every time a policy is up for renewal, compares 10
leading options from 10 different carriers and submits the best one based on the customer’s
profile year by year.
3.1.4

Distribution and advice

49.
Insurers can use consumer and market insights gained from BDA to extend the reachand scale of their distribution capability beyond their existing traditional branch networks andcall centres. This extended capability presents opportunities for access to wider consumer
bases, product updates and cross-selling, potentially resulting in more choice for consumers.
50.
Insurers can use BDA insights to evaluate customers’ past behaviour, recent actions
and needs in order to deliver the right message, at the right time, and via the rightcommunication channel. This can facilitate an enhanced customer interaction process throughappropriately tailored communication and the use of an optimal distribution channel mix.
51.
Some insurers are pursuing single direct platforms to address the growing needs oftheir digital-only customers in a holistic manner by consolidating various sources of data into
a “single view of the customer”. All product distribution, as well as the bulk of their marketing,sales, underwriting and support, is accomplished using a single digital platform. This can allow
insurers to accurately develop business volume forecasts and customer lifetime value
estimations, and define customer loyalty and retention campaigns, sometimes with real-time
response capabilities.
Example:FranceSeveral insurance groups have begun moving to cross-channel content strategies (“omnichannel”) in order to give their customers convenience when they decide to interact withthem. One insurer is employing artificial intelligence (AI) and data analytics to better
understand its sales processes. It is using these insights to develop systems that are more
appealing to customers and more rewarding for its agents and partners.
52.
Insurers may also utilise their customer base to curate platforms that offer
complementary products, by using insights gained from BDA to make non-insurance products
and services available to customers alongside insurance products. Insurers can also offer
these insights to other businesses, either within their broader group structures or externally,that may wish to offer other products. Additionally, BDA insights gained from partnerships with
non-insurance businesses could enable insurers to offer correlated insurance products (egIssues Paper on the Use of Big Data Analytics in Insurance
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customers having the option to purchase travel insurance directly with the insurer whenbooking a flight through a partner airline or travel agent).
53.
Insurers engaging in these types of cross-selling strategies would need to take into
account applicable data protection laws and the importance of avoiding or properly managing
potential conflicts of interest that may emanate from such partnerships. Additionally, bundlingof insurance and non-insurance products should not unduly limit customers’ choices, orcompel them to purchase packaged solutions that include one or more products having little
or no value to them.
Example:Australia
In 2016 an Australian bancassurer announced its intention to develop a customer
marketplace online portal to offer its customers products manufactured, and services
offered, by other companies as well as its own. The marketplace portal is premised on a
strategy that recognises that customer value creation is not limited to products
manufactured by the insurer itself. The centrepiece of the strategy is a mobile applicationthat went live in 2018, and at that time the marketplace included links to businesses suchas those in conveyancing and car maintenance.
3.2
3.2.1

Pricing and underwriting
Increased granularity in risk selection and pricing

54.
Historically certain risks would have been associated with very high fixed costs so that
insuring them would not be profitable or economically viable, or products would need to be
priced so high resulting in the demand being too low to justify such offerings. BDA provides
insurers with the ability to insure certain previously “uninsurable” risks at an affordable price. 17
For example, through automated evaluation of complex, high-volume data sets on the
progression of diseases, it could become possible to further extend life insurance cover to
HIV-positive customers, or to offer such cover more quickly and/or at lower rates. The same
could apply to homeowner insurance for buildings in flood and disaster-prone areas. Usingsatellite pictures and geo data, insurers can identify and assess such areas more precisely.
Customers who may want to take up these policies at lower rates offered by insurers who
have access to these types of BDA insights may, however, sometimes be unaware of theselower rates. 18
55.
On the other hand, granular individual-based approaches to pricing could also
negatively affect individual insurance premiums, potentially raising affordability concerns.
Whilst the consideration of factors affecting affordability is not new, historically it has tended
to be limited to easily identifiable population demographics, such as geography or age.
However, the use of BDA may change this. For example, affordability may depend on driving
patterns which are a result of an individual’s personal situation (eg shift workers driving atnight, parents dropping children at school in the morning). This is particularly concerningwhere insurance is, in effect, an essential service or compulsory. This could raise policy level
questions, particularly where insurance access levels are low, on the need to balance

17 https://www.bafin.de/SharedDocs/Downloads/EN/dl_bdai_studie_en.html at page100.
18https://www.fca.org.uk/publication/feedback/fs18-01.pdf .

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appropriate levels of granularity for risk categorisation purposes with the potentially greatersocial need to ensure the affordability and inclusiveness of insurance products.
Examples:Netherlands
A Dutch insurer is investigating whether it can determine car insurance premiums bymachine learning. Traditionally, a policyholder’s premium is calculated in a linear way. For
example, both the age of the car and the postal code of the area where the owner lives isused to determine the appropriateness of the premium. With algorithms and machinelearning, the calculation of the premium happens in a dynamic way, for example
investigating the interaction between the age of the car and the postal code. Other variables
can be taken into account as well, such as the customer’s driving behaviour and previous
damage. The idea is that as the algorithm learns from the data, it will become increasingly
accurate in determining the risk of damage (and therefore the premium).
56.
One study predicts that the very nature of risk could change as a result of reduced
uncertainty due to ongoing improvements in tracking and predictive technologies. 19 As aconsequence premiums could come under pressure, potentially shrinking revenue streams,
especially if insurers are unable to adequately make up for expected shortfalls through othermeans (eg reducing costs by improving operational efficiencies or implementing other loss
mitigation strategies). The application of BDA in pricing could, therefore, increase the incentive
for insurers to exploit differences in premium levels, including through the practice of priceoptimisation as discussed below.
3.2.2

Price optimisation

57.
Price optimisation is the practice of determining the “ideal” or highest premium that canbe charged to a particular customer based on a variety of individualised factors other than just
risk.
58.
Price optimisation has always occurred in some form or another in insurance. In thepast it was a case of underwriters using their discretion and judgement to adjust prices. 20 Thiswas often done at a customer-segment level or across an entire portfolio of risks. The use of
BDA has introduced the ability to adjust prices at a more granular customer-specific level. 2159.
This can be done in two ways. First, algorithms and advanced statistical modelling canbe applied to traditional data sources. Second, those algorithms and advanced statisticalmodelling techniques can be applied to new sources such as IoT data and online media data.
60.
Linking financial transaction and behavioural data with data on preferences and needs
can provide insights on a customer's willingness and ability to pay. Data potentially sourced
from different and unrelated contexts may show that Customer X would pay any price for aparticular product while Customer Y would be very price conscious. If a customer’s demand
for an insurance product is high or the customer is inert and/or unlikely to shop around the risk
price will be increased. Alternatively, if the customer’s demand for an insurance product is low
19https://www.mckinsey.com/industries/financial-services/our-insights/insurance-beyond-digital-the-rise-of-

ecosystems-and-platforms .
20NAIC

White
Paper
on
Price
Optimization
atparagraph5.
See
https://www.naic.org/documents/committees_c_catf_related_price_optimization_white_paper.pdf .
21NAIC White paper at paragraph 6.
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and they are more likely to be flexible, the risk price will be reduced. In each circumstance thespecific customer would likely be asked to pay the maximum price they are prepared to pay
based on the insights accumulated from the use of BDA, resulting in differential pricing for thesame or similar products across different customers. In other words, the practice of priceoptimisation can result in two similarly situated customers paying different premiums, even if
they have the same loss history and risk profile.
61.
The use of BDA in this way gives rise to many potential concerns including a lack ofdisclosure and transparency in pricing, and issues around affordability, inclusion and possibly
unlawful discrimination. It also raises fairness questions about the potential exploitation ofcustomer inertia which is sometimes a result of customers having limited time, ability oropportunity to shop around for cheaper products. This concern would be heightened in casesof vulnerable consumer groups (eg elderly customers and/or customers with lower income or
education levels). Insurers benefiting from this type of premium differential (at an individuallevel) may diminish the overall value of insurance by emphasising price over quality of cover.
This could negatively impact consumer trust thereby causing reputational risks for the
insurance sector.
Examples:United States
In the US, state insurance supervisors are concerned that price optimisation may be a
departure from traditional cost-based rate making and a move towards rate making basedin part on customers' price sensitivity. Consequently, a number of states have taken preemptive action to ban the practice. Several states issued bulletins stating that priceoptimisation results in rates that are unfairly discriminatory. Some of these states are also
requiring insurers to remove price optimisation factors from rate filings. Additionally, theNational Association of Insurance Commissioners (NAIC) adopted a Price Optimization
White Paper 22 that provides background information on state rating law, actuarial principles,and price optimisation, including an overview of various definitions of price optimisation
used by stakeholders. The White Paper also identifies potential benefits and drawbacks ofprice optimisation, which includes concerns raised by consumer advocacy groups.
United Kingdom
In the UK, the pricing practices of insurers are a focus of regulatory attention, in particulardifferential prices for new and renewing customers of home and motor insurance. In October
2018 the FCA published a discussion paper on fair pricing in financial services 23 andlaunched a market study into general insurance pricing practices 24. The market study islooking at the scale of any harm to customers from general insurance pricing practices, who
it affects and, if required, what actions are required to improve the market. An interim market
study report was published in October 2019. 25 The final report is anticipated during the firstquarter of 2020, alongside a consultation paper on any proposed remedies.
Netherlands
Comparison websites in the Netherlands indicate that insurers are increasingly investigating
the use of dynamic pricing and, on a smaller scale, dynamic characteristics, so that both the
22 See footnote 20.

23 https://www.fca.org.uk/publications/discussion-papers/dp18-9-fair-pricing-financial-services .

24 https://www.fca.org.uk/publications/market-studies/ms18-1-general-insurance-pricing-practices-market-study .
25 https://www.fca.org.uk/publication/market-studies/ms18-1-2-interim-report.pdf .
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price and policy conditions are determined ‘on demand’. This brings challenges forsupervisors. For example, there are concerns about how careful development of an
insurance product can be monitored or supervised if the terms of the insurance policy differ
from customer to customer.
India
Differential pricing on the basis of customer demand and/or willingness to pay is not allowedin India as product pricing is subject to regulatory approval by the Insurance Regulatory andDevelopment Authority (IRDA). Premiums may be varied based only on narrowly defined
rating criteria.
3.2.3

Underwriting

62.
The use of BDA in underwriting can have a number of benefits for customers in helping
identify, assess and potentially reduce their risks. It creates opportunities for risk mitigationand loss prevention partnerships between insurers and customers. BDA can enable insurers
to provide feedback to customers on potentially risky behaviour and incentivise them to modify
behaviour, thereby lowering their risk exposure. Insurers can also notify customers in real timeof changes to their risk profile or when certain behaviours or an activity they are planning or
engaged in may give rise to risks that can be mitigated with new insurance cover or
adjustments to existing cover. For example, an insurer using BDA applied to social media
updates and location tracking to identify that a customer is about to go on holiday and mightneed travel insurance, or if they need enhancements to their travel insurance with specificcoverage such as when the customer is looking to undertake more hazardous activities suchas skiing or mountain climbing. BDA may also assist customers by providing solutions to help
them better understand the size of their risk exposure and the amount of coverage that would
be appropriate to cover that exposure.
63.
The application of BDA also offers new solutions to the challenge of underinsurance,
including through the development of a new generation of insurance calculators offered atpoint of sale to aid customers to estimate the cost of repairing their homes. For example, BDA
can be used to draw on and analyse large volumes of data about a particular location, its
susceptibility to major disasters, weather patterns and the cost of repairs based on demand
surge costs rather than at normal times to better estimate the cost of rebuilding a specificcustomer’s home. However, such calculation processes should have appropriate levels oftransparency, while balancing proprietary interests, so that customers’ understanding of the
final estimate is not unduly impaired.
Examples:Netherlands
In the Netherlands, insurers make use of the services of an information technology company
that calculates the reconstruction value of a home under a property insurance policy.
Traditionally, this value was estimated based on year of construction, the cubic metres, thetype of house and the construction. However, this company claims to have access to morethan a hundred other variables that can all be used for a more adequate estimation than the
traditional variables. In some cases, consumers were confronted with higher premiums,
based on the new, automated way of calculating. Although algorithmic calculations might
be more accurate for individual risks than the traditional way of calculating, algorithms anddata are not always flawless. This shows that insurers have to make sure that the outcomesof an automated system are both explainable and traceable.
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Turkey
In Turkey, the Natural Disaster Insurance Institution provides compulsory earthquake
insurance through the Turkish National Catastrophe Insurance Pool (TCIP) 26, utilising
specific data provided by customers. This data includes the address, gross area, building
year and construction type of the dwelling. After receiving the required data, an algorithm
calculates the insurance premium automatically. A calculation tool is available to customers
on the TCIP’s website which makes it possible to calculate the insurance premium before
issuance of a policy. 27
64.
As product development becomes progressively dependent on the availability of
personalised customer insights there is a risk that customers who do not have access to digital
devices or do not want to provide personal data (beyond what is legally required) may become
marginalised and even excluded from insurance. While the widespread use and increasing
portability of digital devices has created significant opportunities to broaden the reach of
insurance to new or previously underserved markets, some customers simply cannot afford
smart devices, do not have reliable or affordable access to the internet, or do not share data
online. Other customers may simply not wish to provide arguably sensitive, personalised data
about themselves to an insurer or outsourced third party. Additionally, some customers may
have challenges with, or be resistant to, using innovative technologies.
65.
There may also be potential for vulnerable high-risk customers to become uninsurable.
The use of BDA enables insurers to personalise product offerings and to identify whichcustomers are higher risk and therefore more likely to claim. In the interest of maximising profit
margins, insurers may be less likely to provide cover to customers in instances where BDA
reveals such customers to be more likely to claim or unable to consistently pay the premiums
necessary to insure their risks, possibly due to wider socio-economic factors. This could resultin financial exclusion, under-insurance and the creation of an uninsurable subset of customers.
This is particularly concerning where insurance is, in effect, an essential service or compulsory
and/or in regions with low levels of access to insurance. As discussed in section 3.2.1, in somejurisdictions this may require broader policy level consideration of appropriate strategies tofacilitate inclusion while recognising the necessity of risk-based pricing to preserve thesustainability of the sector.
Example:Australia
In its recently published North Australia Insurance Inquiry Interim Report 28 the AustralianCompetition and Consumer Commission (ACCC) observed that many insurers wereseeking to reduce their exposure to high risk customers by actively not competing. This wascreating accessibility and affordability issues in North Australian home insurance markets.
Insurers used BDA technology to identify customers whose residences were in areas highly
susceptible to cyclone and flood risk from multiple data sources including geographicalsurveys and historical weather reports. Insurers would then actively seek to reduce their
exposure to these customer groups either directly by not writing new business in these areas
or indirectly by increasing prices until their exposure decreased.
26 https://dask.gov.tr/tcip/ .

27 https://www.dask.gov.tr/e-services/portal/calculatePremiumEng .

28https://www.accc.gov.au/focus-areas/inquiries/northern-australia-insurance-inquiry.

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66.
It is possible to predict with increasing accuracy a customer’s life expectancy andlikelihood of sickness using genetic data. BDA applications also have the potential to expand
beyond genetic data. 29 These types of insights can potentially make large segments of society
uninsurable. Insurers’ use of genetic data and probabilistic models raises concerns about
potential bias and uninsurability. In many jurisdictions legislation or industry codes of practice
restrict the use of genetic and related data for insurance purposes.
Examples:CanadaIn Canada the Genetic Non-Discrimination Act, S.C. 2017, c. 3 has been in force since2017. 30 The Act prohibits people and businesses from requiring the results of genetic tests
when providing goods and services, entering into contractual agreements, or offering
specific terms or conditions in contracts, including insurance policies. The Act also prohibits
the denial of services to individuals who refuse to undergo genetic tests and requires writtenconsent for the collection, use and disclosure of individuals’ genetic test results. However,the constitutionality of the Act was challenged before the Supreme Court of Canada in
October 2019. 31 Judgment in the matter has been reserved.
United Kingdom
In October 2018 the Association of British Insurers (ABI) and the UK Government publisheda code of practice on the role of genetic testing in insurance. 32
Germany
The use of genetic data in the insurance sector is strictly regulated. In private healthinsurance the insurer may not, either before or after the conclusion of the insurance contract,
require genetic tests or analysis to be carried out or require the communication of results ordata from genetic tests or analysis already carried out, or receive or use such results ordata. This does not apply to life insurance, occupational disability insurance, occupational
disability insurance and long-term care insurance if a benefit of more than €300,000 or morethan €30,000 per annum is agreed. 33
Australia
In October 2018 the Australian Financial Services Council (trade body for the life insurance
industry) announced that it was consulting on a moratorium to stop the use of genetic test
results as part of insurance applications, enabling every Australian to get up to $500,000
worth of life cover without having to disclose adverse test results. 34 On 21 June 2019 it
released FSC Standard 11: Moratorium on genetic tests in life insurance, which takes effecton 1 July 2019. 35
29https://www.theverge.com/2018/2/19/17027902/google-verily-ai-algorithm-eye-scan-heart-disease-cardiovascular-risk .
30
https://laws-lois.justice.gc.ca/eng/acts/G-2.5/page-1.html .
31https://www.canadianunderwriter.ca/legal/why-supreme-court-of-canada-is-looking-at-risk-basedunderwriting-1004169852/ .
32
https://www.abi.org.uk/globalassets/files/publications/public/genetics/code-on-genetic-testing-andinsurance_embargoed.pdf; https://www.abi.org.uk/news/news-articles/2018/10/abi-and-governmentpublish-updated-code-on-genetic-testing--insurance/.
33 §§18 GenDG, 213 VVG.
34 https://www.fsc.org.au/policy/life-insurance .
35https://www.fsc.org.au/news/media-releases .
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Switzerland
The Swiss Federal Act on Genetic Testing on Humans places restrictions on the use ofgenetic data in insurance. 36 In general, for socially important insurance, the use of such data
is prohibited. For other types of insurance cover, the use is strictly regulated. In third-party
liability cases it is generally forbidden to use genetic data or to ask for genetic screening (egfor the assessment of claims).
United States
In the US, the Genetic Information Nondiscrimination Act of 2008 (GINA) prohibits the useof genetic information in decisions relating to health insurance eligibility or coverage. 37
While this prohibition does not apply to other forms of insurance (eg disability, long-termcare or life insurance), some individual states have passed laws prohibiting the use ofgenetic data in other types of insurance. 38
3.3

Claims handling

3.3.1

Risk mitigation and loss reduction

67.
One way insurers can use BDA to improve claims handling is to help customers avoid
having to make claims in the first place. By using BDA to assess individual risk and claim
behaviours more accurately, customers can be nudged, post-sale, towards decisions andactions aimed at reducing the likelihood of risks materialising or to mitigate potential losses inthe event that such risks do materialise. Examples of these include improving driving habits,
making healthier lifestyle choices or taking preventative measures as a result of early warnings
about poor weather or other hazardous conditions.
68.
While insurers have relied predominantly on historical data for actuarial calculation andrisk modelling purposes, they are now able to rely on data sources that are real time or evenforward-looking and may result in loss reduction. For example, "smart drivers" continuously
produce data while they are driving (eg through the use of telematics devices and other “onboard” vehicle computer systems that provide instantaneous feedback on dangerous driving
behaviours). The availability of this type of data can reduce the risk of accidents and potential
multi-claimants, resulting in claims savings for insurers. In return, customers who use thesesmart devices may receive better insurance rates and potentially benefit from improved risk
behaviours such as better driving.
69.
The monitoring and analysis of data from IoT sources such as sensors and otherconnected devices in homes and businesses, coupled with alerts to customers on potential
risk events, is also a strong enabler for proactive loss prevention and may result in reductionsin the frequency and severity of claims and associated claims costs. Customers in turn may
benefit from premium reductions due to their decreased risk exposure. Similar to the use oftelematics devices for UBI purposes alluded to earlier, any monitoring of customer behaviour
through IoT sources must not result in customers’ privacy rights being compromised in
exchange for insurance coverage options.
Examples:Various36 https://www.admin.ch/opc/en/classified-compilation/20011087/201401010000/810.12.pdf .
37 https://www.congress.gov/110/plaws/publ233/PLAW-110publ233.pdf .

38 https://www.genome.gov/about-genomics/policy-issues/Genetic-Discrimination .

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Insurers in various jurisdictions offer customers rewards or discounts on their car insurance
premiums if they drive safely. Their driving behaviour is measured by a device that is plugged
into the car or other connected solutions. These products could lower overall damage rates,
as customers have an incentive to drive safely.
The connected home market has also attracted several major multinational equipment andservice providers. Many global technology and media companies are leveraging theirextensive presence in the entertainment market to deliver online security and safety
products. Some insurers have also introduced successful connected home solutions that go
beyond insurance and offer threat prevention and safety alerts.
The connected health and fitness market is also growing fast. There are more and more
portable health devices attached to people around the world daily. Globally several insurers
have launched connected solutions designed to improve the wellbeing of their customers.
Most, if not all, of these offerings are fully digital. They leverage the capabilities of analytics,
mobile technology and social media channels to offer real-time protection rather than justindemnification.
Some life insurers have introduced features in their mobile apps to encourage healthy living
and increase opportunities to engage with customers.
70.
Behavioural changes and improved risk mitigation can contribute to greater societal
issues beyond insurance. Improved awareness and prevention of dangerous driving habits
may reduce motor vehicle accidents and fatalities. Likewise, BDA insights that encourage
healthier lifestyles may reduce pressure on publicly-funded emergency and medical services
and reliance on safety nets such as social welfare and public healthcare. The broader societal
benefits of these insights must be carefully weighed against possible infringements of therights of individual customers who may not be aware of, or comfortable with, this level ofscrutiny into their daily lives by insurers.
3.3.2

Claims processing

71.
The use of BDA in claims processing can lead to a number of efficiency gains resulting
in reduced costs for insurers and customers. The claims settlement process could be improved
through (partially) automated claims approvals or rejections, subject to appropriate
consideration being given to fair customer outcomes. For example, digital loss assessment
and repair can occur on the basis of picture recognition or sensor data in combination withinvoice data for more accurate pay out calculations. Drone technology is also being used inmany instances for video inspection of damages to assess claims quicker and with moreaccuracy.
Example:Germany
In Germany most insurers offer apps in private health insurance for claims notification andprocessing based on pictures of receipts.
A German agricultural loss insurer is using drone technology to automatically evaluate
damages based on images taken by the drones. However, the insurer also regards thehuman factor as important in the assessment process.

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72.
The use of BDA can also allow insurers to calculate and produce claims pay outs
quicker and more accurately based on historical claims data, allowing for the fast tracking ofcertain types of claims with minimal friction for the customer.
Examples:Singapore
In August 2018, an insurer tested a blockchain-based insurance solution that offeredpregnant women financial protection in the case of gestational diabetes. Through an app on
their mobile device, the product can connect securely to a customer’s electronic medical
records to perform parametric underwriting. If a customer is eligible, a “smart contract” policy
is immediately generated. Upon diagnosis, the customer’s medical data triggers anautomated benefit payment with no further action required. This improves data security as
the insurer does not require access to the underlying medical data to confirm insurability or
to review claims.
In June 2019, this solution was piloted with the country’s largest newspaper and a major
Singapore-based insurer to automate the life insurance claim verification process. Whenfamily members file an obituary with the newspaper, they will be informed about the productand upon their consent, the deceased’s national identity number will be submitted to theBlockchain as hashed data to search for a matching life insurance policy. If a match is found,an automatic notification will be sent to the insurer to initiate the claims process.
United Kingdom
Parametric insurance is increasing in prevalence across the sector. Agreeing to payments
up front can be beneficial for some customers over traditional insurance products byproviding greater certainty and speed for claims payments. In the UK an insurer has
developed a flood insurance product that involves an instant pay-out of a pre-agreed amountbeing triggered when flood water reaches a pre-agreed depth on the sensor installed by the
insurer at the property. Similar models are also used in flight delay and cancellation
insurance products, where integration with a data feed providing live data on flight status
enables the near instant pay-out of a claim in the event of a flight being delayed or cancelled.
73.
Insurers may need to consider the level of transparency and customer understandingof the algorithm process and final outcome. This would be particularly relevant if the algorithm
is also used to reject a claim or determine a partial settlement.
74.
Generally, specialised claims are at risk of being settled incorrectly when there is anover reliance on generic claims settlement models, which can result in negative outcomes for
insurers and customers. The use of BDA to develop claims settlement models, without
appropriate oversight and transparency mechanisms, could increase the likelihood ofsettlement algorithms mapping customers into certain categories based on unsupported
associations or biases. This could potentially cause an increase in unsubstantiated rejections
and unlawful discrimination in the claims decision making process, resulting in a loss ofcustomer confidence and possible reputational damage for insurers.
75.
Additionally, insurers may use historical insights generated by BDA as triggers todetermine earlier which claims are more likely to result in disputes or litigation and respondaccordingly, potentially speeding up the escalation process for more controversial or complex
claims. However, algorithms used to trigger potentially suspicious or litigious claims could be
at risk of perpetuating historical claims settlement biases that may need to be appropriately
managed.
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76.
Claims optimisation or claims settlement optimisation is the practice of using BDA to
identify claimants who are likely to accept cash settlement amounts below the true value of
their claims and then use that insight to negotiate an “ideal” or minimum claims settlement
value. It can enable insurers to settle claims based on customer vulnerability and other nonloss related factors rather than the actual quantifiable loss amount. 39 It is currently unclear
how widespread the practice is. However, given that it essentially relies on the same approachand technology as price optimisation, it follows that insurers may need to further consider how
to deal with potentially similar issues in the claims settlement process.
3.3.3

Fraud detection

77.
The insurance sector is constantly dealing with the impact of fraudulent claims. The
costs associated with fraudulent losses and fraud detection tools are often passed on tocustomers. Customers can also experience significant friction during fraud investigation
processes which may lead to adverse psychological or financial consequences.
Examples:USA
Insurance industry estimates generally put fraud at about 10 percent of property andcasualty insurers’ incurred losses and loss adjustment expenses each year, although thefigure can fluctuate based on line of business, economic conditions and other factors. 40
Using this measure, over the five-year period from 2013 to 2017, property and casualty
fraud amounted to about $30 billion each year. 41
Australia
The Insurance Council of Australia (industry body for Australian general insurers) has statedthat fraudulent claims cost insurers up to $2 billion annually. 42
Germany
According to industry reports about nine percent of reported losses in motor, liability andproperty insurance show inconsistencies, meaning that the damage description does notmatch the picture of the damage, or the persons involved provide contradictory data or
submit manipulated receipts. While not every inconsistent damage report is a case of
insurance fraud, insurers are encouraged to examine inconsistent claims more closely. In
2017 property and casualty insurers estimated that insurance fraudsters caused overall
losses of €4 - €5 billion a year. 43
Switzerland

39Duncan Minty, 7 reasons why claims optimisation needs to be seen as a failure (22 March 2018)https://ethicsandinsurance.info/2018/03/22/claims-optimisation-2/ .
40Estimate based on research conducted by the Battelle Seattle Research Center for the Insurance
Information Institute in 1992 (Fighting the Hidden Crime: A National Agenda to Combat Insurance
Fraud. Insurance Information Institute, March 1992) and other industry reports (including Insurance
Fraud, Renewing the Crusade, Conning, 2001).
41Insurance
Information
Institute,
Backgroundon:
Insurance
Fraud”
https://www.iii.org/article/background-on-insurance-fraud .
42Chris Sheady, “Money for Nothing” at https://insuranceandrisk.com.au/article/money-for-nothing/ .
43 https://www.gdv.de/de/themen/news/fast-jede-zehnte-schadenmeldung-mit-ungereimtheiten-11376.

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According to an industry study undertaken by the Swiss Insurance Association 44 in 2017about ten percent of reported losses in general insurance are fraudulent.
78.
Traditionally insurers have applied a combination of rule-based indicators and scoringmodels to identify claims with an increased likelihood of fraud, alongside random checks (eginternal audit) to manage fraud. 45
79.
The above methods have two major challenges. First, they rely on manualinterventions which give rise to the risk of human error and unnecessarily long lead times fromfraud detection to the settlement of claims. Second, they are also known for high false-positive
rates, which could lead to adverse outcomes for customers. 46
80.
BDA, when used across the entire product and claims management lifecycle, canprovide insurers with the ability to identify fraud in a more holistic and sophisticated manner
through early warning triggers and predictive modelling. This would be particularly helpful in
pre-emptively identifying and managing potential fraud perpetrated by criminal syndicates andother organised efforts. 47
81.
Early warning triggers and more efficient fraud detection capabilities enabled throughthe use of BDA could significantly reduce operational and claims costs, potentially resulting inlower insurance prices for customers. BDA could also be used by insurers to assist law
enforcement agencies to identify, track and deal with perpetual offenders within the system.
Examples:India
The Insurance Information Bureau of India 48 (IIB) offers an underwriting and claims searchengine to all partner life insurers to enhance underwriting efficiency and mitigate fraud. IIB
is also working on a risk scoring and predictive model for life insurance that can be used toprovide insights for underwriting and fraud detection. Certain life insurers are using creditscores/history in financial underwriting and consent based data authentication throughpartner systems. Geotagging and facial recognition technology is being used for pre-issue
verification and fraud mitigation purposes.
South Africa
The South African Insurance Crime Bureau (SAICB), by accessing multiple data sources
and through the use of advanced analytics, provides a collaboration platform for insurers,
supervisors, law enforcement agencies and other stakeholders to help prevent, detect, track
and prosecute fraud and related crimes in the non-life insurance sector. 49
Hong Kong

44 https://www.svv.ch/en .

45 WNS, “WNS Decision Point: Insurance Fraud Detection and Prevention in the Era of Big Data –Curbing
Fraud
byApplicationofAdvanced
Analytics
Across
PolicyLifecycle”
https://www.wnsdecisionpoint.com/Portals/0/Documents/Reports/PDFFiles/514/38/WNS%20Decision
Point_Report_Fighting%20Insurance%20Fraud%20with%20Big%20Data%20Analytics.pdf at page 8.
46 WNS Decision Point at page 8.
47https://www.eslinsurancemonitor.nsw.gov.au/sites/default/files/Big%20Data%20Occcasional%20Pa
per%20FINAL_0.pdf at pages 27 – 29.
48 https://iib.gov.in/ .
49 https://www.saicb.co.za/ .

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The Hong Kong Federation of Insurers (HKFI) launched the Insurance Fraud Prevention
Claims Database in 2018. 50 The system applies artificial intelligence and BDA technology,
which assists claims officers to detect different types of suspicious claims or potential
insurance fraud, particularly those involving multiple claims and syndicates. The systemcurrently covers motor vehicle, health and personal accident insurance, and will extend its
coverage to life and travel insurance in the next phase.
Singapore
Since January 2017, Singapore’s General Insurance Association (GIA) has implemented a
Fraud Management System that utilises AI and data analytics to scrutinise motor insurance
claims. 51 This has allowed insurers to focus on the most suspicious cases and to investigate
these claims in greater detail.

50 https://insuranceasianews.com/hong-kong-launches-claims-fraud-database/ .
51 https://www.ethozgroup.com/blog/rise-insurance-fraud-cases-implications/ .

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4Supervisory considerations

82.
This paper has highlighted a number of potential benefits and risks associated with theuse of BDA across the insurance product life cycle. Understanding these benefits and risks
can help supervisors to develop appropriate and proportionate responses to rapid
advancements in BDA enabling technologies and applications in a manner that ultimately
promotes and encourages the consistent delivery of fair outcomes to customers.
Examples:Australia
In June 2018 the New South Wales Insurance Monitor published a paper that exploredissues around the use of BDA by general insurers. The paper considered the potential
implications of this usage for market competition and consumers as well as for regulatorsand policymakers. 52
Germany
In July 2018 the Bundesanstalt für Finanzdienstleistungsaufsicht (BaFin) published areport on Big Data and Artificial Intelligence, representing the outcomes of research done
earlier with Partnerschaft Deutschland, the Fraunhofer Institute for Intelligent Analysis andInformation Systems and the Boston Consulting Group. 53
United States
The US Federal Insurance Office (FIO), in its 2017 Annual Report, published details of
various technological innovations in the insurance sector including potential risks andbenefits associated with the manner in which US insurers are applying BDA across theinsurance product life cycle. 54 The report also contains examples of possible supervisory
responses in this regard.
The Federal Advisory Committee on Insurance (FACI), which provides advice andrecommendations to the FIO, will be conducting an in-depth examination of BDA throughits subcommittee dedicated to the Availability of Insurance Products. 55 This subcommittee
has a consumer focus and is expected to focus on topics such as transparency in the useof data and algorithms, price optimisation, availability in light of increased granularity inrisk selection and pricing, and discriminatory impact.
83.
The fairness outcomes envisaged in ICPs 18 and 19 must be achieved irrespective of
the adoption of new technologies or other innovations by insurers, including the use of BDA.
However the means by which these outcomes are achieved, and the appropriate supervisory
responses, may differ depending on the manner in which the specific innovations are applied
and the impact, likelihood and complexity of any new customer risks that are introduced as aresult.
84.
The previous Digitalisation Paper discussed in detail a number of key challenges andconsiderations for insurance supervisors in attempting to maintain a balanced approach tohttps://www.eslinsurancemonitor.nsw.gov.au/sites/default/files/Big%20Data%20Occcasional%20Paper%2
0FINAL_0.pdf .
53
https://www.bafin.de/SharedDocs/Downloads/EN/dl_bdai_studie_en.html .
54https://www.treasury.gov/initiatives/fio/reports-and-notices/Documents/2017_FIO_Annual_Report.pdf at
52

pages 65 to 70.
55 https://www.treasury.gov/initiatives/fio/Documents/June2019FACI_Minutes.pdf .
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facilitating digital innovation while ensuring adequate levels of consumer protection. 56 Thissection is not intended to repeat those general observations, but rather highlights a few
supplementary issues for supervisors to consider in responding specifically to the use of BDA
in insurance as described in this paper.
85.
These issues will require balancing a number of important interests, including thepromotion of innovations in BDA that enable broader access to insurance, the need of insurers
to safeguard their proprietary commercial information and the right of customers to fair andethical use of their data. Ongoing dialogue and collaboration with stakeholders, including
policymakers, market participants, consumer advocates and other standard setting bodies,
will be critical in helping supervisors gain deeper insights on, and respond more effectively to,this challenge.
4.1

Suitability, affordability and availability of insurance cover86.
As highlighted in section 3, the increasing use of BDA may enable insurers to segment
customers into more granular categories for potentially differential treatment in respect of risk
selection, marketing, underwriting, pricing and claims settlement. Access to bigger data sets
may provide deeper insights to allow more individual customisation than under traditional
insurance (underwriting) practices. This can result in benefits and challenges of which
supervisors should be aware.
87.
Granular customisation may provide more product availability and accessibility, butsome customers may find themselves not being offered specific types of cover at all, or facehigher non-risk related individual premiums and potentially discriminatory claims settlement
decisions. This could adversely impact the availability or affordability of insurance for certain
segments of the population and may result in decreased levels of consumer confidence in theinsurance sector. In this regard, supervisors may also need to consider whether certain BDA
applications could potentially undermine the underlying risk pooling characteristics of
insurance.
Example:France
The collection of data and possible exclusion of customers is not a new phenomenon due
to the emergence of BDA. However developments in BDA technology have amplified theissues tremendously due to the ability of insurers to now collect and process significantly
larger amounts of data at much greater scale and speed. In France the “loi EVIN n° 891009” enacted in 1989, prohibits insurers from using customers’ personal health data intheir risk assessment activities when providing mandatory health insurance. Thisrequirement is still relevant and applicable in a BDA context. However, BDA can enable
insurers to deduce some of the health features of customers through the sourcing andprocessing of new data sets. These practices may therefore require specific regulatory andsupervisory responses.
88.
Increasing use of BDA by insurers has the potential to strengthen the insurance market
through more competitive pricing, wider availability of coverage and innovative product design.
However, the use of BDA could result in consumers having fewer choices of providers. For
example, insurers with strong existing customer bases and significant investment capability
56 IAIS Digitalisation Paper at pages 24 - 33.

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and knowledge resources could leverage their enhanced BDA capabilities to control andmonetise access to customers and customer data. This could potentially create “winner takes
all” network effects for one or a few companies (or platforms), resulting in limited options for
consumers at least in the mass market. Therefore, insurance supervisors may need to engage
more broadly with the relevant anti-competition agencies in their jurisdictions.
89.
Increasingly in some, but not all, jurisdictions issues around access, inclusion andaffordability are closely linked with the overall mandate of conduct supervisors and may
require heightened focus as a result of insights gained through the use of BDA by insurers
and supervisors themselves.
90.
There may also be opportunities for supervisors to obtain insights from data collected
and shared by insurers to assess the effectiveness of advice and suitability of products offeredto customers. Supervisors could compare these insights with other metrics, such ascomplaints rates or product related key performance indicators to assess the effectiveness ofadvice and product suitability after products have been sold.
Examples:Netherlands
In the Netherlands a few new entrants focus their marketing and brand campaigns on
specific target groups (eg high education, millennials and others) to attract potentially low
risk customers.
To monitor the potential pressure on access to insurance in 2017 the Dutch Association of
Insurers initiated a so-called solidarity monitor to analyse the development of the premium
spread and the extent to which consumers remain insurable. This analysis will be conducted
on a yearly basis to monitor developments.
European UnionEIOPA’s recent thematic review on the use of BDA in motor and health insurance
addressed, among other things, the topic of financial exclusion by analysing a number ofindicators such as standard deviation from the average premium, consumer complaints,
rejection rates and number of members in insurability schemes for high-risk consumers.
Based on the information collected EIOPA observed that there is no evidence yet to indicatethat a higher granularity of risk assessment factors excludes high risk consumers. However,while in the analysed sample, BDA tools such as AI or ML were found to already be actively
used by 31% of European insurers across different areas of the insurance value chain, they
expect the impact of BDA to increase in the years to come. The situation, therefore, needs
to be closely monitored.
South Africa
The South African Financial Sector Conduct Authority (FSCA) has, in order to achieve its
supervisory objective, an explicit legislative mandate to promote financial inclusion. 57 To
help execute this mandate, the FSCA has established a dedicated market, customer andinclusion research unit supported by specialist data analytics tools and resources. The unit
will draw on a variety of internal and external data sources to derive focused insights oncustomer behaviours, business model, product, distribution and servicing trends in lower
income customer segments to ensure supervisory responses that appropriately promote
Section 58(1)(e) of the Financial Sector Regulation Act, No. 9 of 2017;
http://www.treasury.gov.za/legislation/acts/2017/Act%209%20of%202017%20FinanSectorRegulation.
pdf .
57

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inclusion and transformation across the financial sector, including insurance. These insights
will be supplemented by analysis of insurer data submitted through statutory returns andthematic data requests designed for conduct risk monitoring purposes. 58
4.2

Governance and oversight of algorithms

91.
Subsection 2.2 of this paper highlights a number of concerns about the increasing
complexity of algorithms and potential lack of transparency in their development and use. Asa starting point to address these concerns, supervisors may consider the applicability ofexisting requirements to the use of algorithms, including general principles relating to the
management of risks taken by the insurer and the fair treatment of customers.
92.
The simplicity, clarity and adequacy of communication and disclosures to customers
in respect of the intended operation of algorithms, how their data will be used and any
associated risks and avenues for recourse will also be important considerations.
93.
Alongside existing requirements, some issues that may require additional
consideration by supervisors in respect of the use of algorithms include the following:•

defining appropriate governance principles for the use of algorithms (eg adoption of codesof good practice and ethical use guidelines);•

measures implemented by insurers to ensure error and bias-free programming ofalgorithms to the extent reasonably possible; and•the reliability of the algorithm, the accuracy and relevance of the specific data sets being
used and their correlation with the specific customer outcomes that are intended to beachieved.

94.
Depending on the complexity and opacity of some algorithm processes, supervisors
may also want to consider taking steps either themselves or through reliance on otherindependent audit or validation parties to conduct, for example, sample verification andintegrity checks both on the algorithm process itself as well as the outcome of the process inensuring fair customer outcomes. Additionally, discussions between supervisors and insurers
during the algorithm development process could be helpful in pre-empting potential
supervisory concerns that may otherwise only be revealed much later.
Examples:FranceThe French Law for a Digital Republic, 59 enacted in October 2016, introduces a principle ofloyalty applicable to digital platforms. These are comprehensively and legally definednotably as “people who offer an online public communication service based on either
ranking or referencing using computer algorithms”.
These platforms are subject to a duty of fair, clear and transparent disclosures to customers
on a range of subjects, in particular the general terms and conditions of use of theintermediation service and the existence of a contractual/capital relationship or
remuneration for their benefit. The law also regulates the disclosures that online platform
operators must provide when offering pricing and other comparison services.

58 https://www.fsca.co.za/Documents/FSCA_Strategy_2018.pdf at page 22.

59 https://www.lexology.com/library/detail.aspx?g=d2f9a06a-bd26-4a7a-9594-0ae639d51bd1 .

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In addition, the law obliges the public administration to inform the persons concerned thatan individual decision has been taken on the basis of an algorithm (via an explicit mention).
European Union
In April 2019, the European Commission published its Ethics Guidelines for Trustworthy
Artificial Intelligence. 60 The Guidelines are based on the following key requirements:•

Human agency and oversight: AI systems should enable equitable societies by
supporting human agency and fundamental rights, and not decrease, limit or
misguide human autonomy.

•

Robustness and safety: Trustworthy AI requires algorithms to be secure, reliable
and robust enough to deal with errors or inconsistencies during all life cycle phasesof AI systems.

•

Privacy and data governance: Citizens should have full control over their own data,
while data concerning them will not be used to harm or discriminate against them.

•Transparency: The traceability of AI systems should be ensured.

•Diversity, non-discrimination and fairness: AI systems should consider the whole
range of human abilities, skills and requirements, and ensure accessibility.

•

Societal and environmental well-being: AI systems should be used to enhance
positive social change and enhance sustainability and ecological responsibility.

•

Accountability: Mechanisms should be put in place to ensure responsibility andaccountability for AI systems and their outcomes.

Additionally, as a follow up to its thematic review on the use of BDA in motor and healthinsurance, EIOPA has created a Consultative Expert Group on digital ethics in insurance. 61
The aim of this stakeholder group is to assist EIOPA in the development of a set of principles
for digital responsibility in insurance, which intends to address, from a fairness and ethicalperspective, the use of new business models, data sources and technologies in insurance,with a particular focus on pricing and underwriting. The stakeholder group may also assist
EIOPA in developing a sound governance framework around the use of BDA tools such as
AI and ML in insurance.
Organisation for Economic Co-operation and Development (OECD)In May 2019, the OECD adopted its Recommendation on Artificial Intelligence setting out anumber of proposed policy areas for consideration by policymakers in respect of big data
and AI, and providing some practical insights on specific areas for enhanced supervisory
focus. 62

60https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai .

61https://eiopa.europa.eu/Pages/News/Call-for-expression-of-interest-EIOPA-Consultative-Expert-

Group-on-digital-ethics.aspx .
62 https://www.oecd.org/going-digital/ai/principles/ .
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The OECD Recommendation identified the following principles for the responsible
stewardship of trustworthy AI:•

AI should benefit people and the planet by driving inclusive growth, sustainable
development and well-being.

•

AI systems should be designed in a way that respects the rule of law, human rights,
democratic values and diversity, and they should include appropriate safeguards –for example, enabling human intervention where necessary – to ensure a fair andjust society.

•

There should be transparency and responsible disclosure around AI systems toensure that people understand AI-based outcomes and can challenge them.

•

AI systems must function in a robust, secure and safe way throughout their life cycles
and potential risks should be continually assessed and managed.

•

Organisations and individuals developing, deploying or operating AI systems shouldbe held accountable for their proper functioning in line with the above principles.

Consistent with these principles, the OECD also provided the following recommendations
to governments:•

Facilitate public and private investment in research and development to spur
innovation in trustworthy AI.

•

Foster accessible AI ecosystems with digital infrastructure and technologies andmechanisms to share data and knowledge.

•

Ensure a policy environment that will open the way to deployment of trustworthy AI
systems.

•

Empower people with the skills for AI and support workers for a fair transition.

•

Co-operate across borders and sectors to progress on responsible stewardship oftrustworthy AI.

In January 2020, the OECD’s Insurance and Private Pensions Committee (IPPC) publisheda report examining the benefits and risks of big data and AI specific to the insurance
industry, in the context of both the OECD Recommendation on Artificial Intelligence and theEuropean Commission’s Ethics Guidelines for Trustworthy AI mentioned above. 63
Singapore
In November 2018, the MAS introduced a set of principles to promote fairness, ethics,
accountability and transparency (FEAT) in the use of artificial intelligence and data analytics(AIDA) in finance. 64 This was co-created with the industry and incorporated feedback from

63 http://www.oecd.org/finance/The-Impact-Big-Data-AI-Insurance-Sector.pdf .

64https://www.mas.gov.sg/publications/monographs-or-information-paper/2018/FEAT .

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financial institutions, industry associations, FinTech firms, technology providers andacademia. Its four key tenets are:•Fairness: AIDA-driven decisions have to be explainable, accurate and justifiable.

•

Ethics: AIDA-driven decisions have to be aligned with the firm’s existing ethicalstandards and minimally be held to the same standards as human driven decisions.

•

Accountability: Financial institutions have to maintain clear responsibility for, andownership of, their AIDA-driven decisions. For instance, they will need appropriate
internal approving authorities for the use of AIDA, and data subjects should haveaccess to channels to provide accurate information about themselves as well as toenquire or seek recourse for AIDA-driven decisions.

•

Transparency: To increase public confidence, insurers must be transparent about
the use of AIDA. This means proactively disclosing to customers as part of generalcommunication, providing explanations of what the data is used for and its
consequences for customers.

Netherlands
In July 2019, the Dutch Central Bank, De Nederlandsche Bank (DNB), published a
discussion paper on General principles for the use of Artificial Intelligence in the financial
sector. 65 The discussion paper contains a set of guidelines for the responsible use of AI byfinancial institutions, including insurers. These guidelines are based on 6 key principles,
namely soundness, accountability, fairness, ethics, skills and transparency (SAFEST). The
principles are described as follows: 66•

Soundness: AI applications in the financial sector should first and foremost be
sound, meaning that they should be reliable and accurate, behave predictably, andoperate within the boundaries of applicable rules and regulations.

•

Accountability: Firms should also be accountable for their use of AI, as AI
applications may not always function as intended and can result in damages for thefirm itself, its customers and/or other relevant stakeholders.

•Fairness: It is vital for society’s trust in the financial sector that AI applications do not
inadvertently disadvantage certain groups of customers.

•

Ethics: As AI applications take on tasks that previously required human intelligence,
ethics becomes increasingly important and firms should ensure that their customers,
as well as other stakeholders, can trust that they are not mistreated or harmed
because of the firm’s deployment of AI.

•

Skills: People at all levels of the firm, from the work floor to the boardroom, shouldhave a sufficient understanding of the strengths and limitations of the AI-enabled
systems with which they work.

65https://www.dnb.nl/en/binaries/General%20principles%20for%20the%20use%20of%20Artificial%20I

ntelligence%20in%20the%20financial%20sector2_tcm47-385055.pdf .
66 https://www.dnb.nl/en/news/news-and-archive/DNBulletin2019/dnb385020.jsp# .
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•

4.3

Transparency: Firms should be able to explain how and why they use AI in theirbusiness processes, and (where reasonably appropriate) how these applications
function.
Third party risk management

95.
There are various ways for insurers to organise the development and operation ofdigitalised processes. The development of algorithms and processing of data for BDA
purposes can be done in-house by the insurer itself as part of its core business operations.
However, third parties are often also used to provide ongoing support for various technicaloperational elements such as cloud services or other platforms, the design of algorithms andprovision or sourcing of large volumes of data not previously available to insurers. Supervisors
may need to consider how insurers manage potential customer risks related to data sharing
with these third parties, specifically in light of ICP 19.12 as described in subsection 4.4 below.
96.
Supervisors may also consider the appropriateness of requiring insurers to extend theirpolicies and procedures on the use of BDA to third party providers as part of their general
governance arrangements for outsourcing where applicable.
97.
Additionally, due to the specialised technical nature of third party services that may beprovided to enable and support the use of BDA, there are sometimes limitations on the numberof service providers available to provide these services in a particular jurisdiction or region.
This could raise concerns for supervisors about the business continuity arrangements of those
providers and the ability of insurers to provide uninterrupted service to customers as a result.
More active supervisory coordination and cooperation within and beyond the jurisdiction or
region may be necessary to facilitate information exchange and to strengthen supervisory
oversight in this regard.
4.4

Issues relating to privacy, data protection and ownership of data98.
The use of customer data and BDA generally raises important issues around privacy,
data protection and ownership of data. These issues are also relevant in the insurance sector,
where the increasing reliance on BDA has the potential to heighten certain risks for individual
customers and insurers.
99.
ICP 19.12 states that “[t]he supervisor requires insurers and intermediaries to havepolicies and procedures for the protection and use of information on customers”. This may
raise certain challenges, because in many jurisdictions the insurance supervisor is not
responsible for regulatory oversight of privacy and data protection issues. Instead this falls
within the mandate of a separate data protection agency. However, insurers and insurance
supervisors in these jurisdictions would still need to consider the privacy related implicationsof customer data usage, and specifically any conduct risks that may arise as a result.
100. Legal questions relating to the ownership of data could arise, for example, when a car's
on-board system transfers data about driving behaviour to the manufacturer who provides thedata to the insurer or when data from a wearable device is transmitted to a health insurer. A
legal dispute could arise between the customer - who is the owner of the car or wearable
device - and the insurer on the ownership of, and entitlement to, the data. In some jurisdictions,customers may be able to raise legal challenges in instances where they are restricted fromaccessing, or sharing, data generated by such devices for their own purposes. Disputes ofIssues Paper on the Use of Big Data Analytics in Insurance
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this nature could have a broader impact, such as resulting in reputational damage for the
insurer and mistrust in the insurance sector as a whole.
101. In cases where data derived from non-insurance contexts is used, conflicts may arise
about the potential lack of consent to, and awareness of, the sharing of this data by thecustomer. Additionally, customers may be adversely impacted as a result of insurers making
decisions that are based on data that is incomplete or inaccurate. If customers are not awareof this type of data being used for insurance purposes, they may not be in a position, or have
had the opportunity, to correct potential inaccuracies at the data source.
102. Therefore, it is important for supervisors to consider the steps taken by insurers to
obtain the necessary consent to collect and use these types of data for specific purposes in amanner consistent with the fair treatment of customers. 67 It would also be useful forsupervisors to collaborate with relevant data protection agencies, other consumer protection
forums and industry bodies in their respective jurisdictions to determine appropriate ways tomitigate potential customer risks arising from the use of BDA for insurance purposes.
Developments relating to data and consumer protection frameworks globally may also provide
helpful insights in this regard. 68
4.5

Other broader considerations

103. The use of BDA in insurance also raises a number of important considerations, which
may or may not fall within the mandate of insurance supervisors, stemming from the impact ofincreasing digitalisation on broader financial market structures, market competition andfinancial stability. Such considerations include, among others, the benefits and risks of nontraditional entrants such as smaller financial technology start-ups (FinTechs) and large wellestablished technology companies (BigTechs), including cloud-based third party data service
providers, into the insurance ecosystem.
104. While detailed exploration of these overarching issues falls outside the scope of thispaper, it is important that insurance supervisors understand the potentially wide-ranging
implications of these developments, from a conduct, prudential and financial stability
perspective. Annex 2 contains a list of some relevant reference material in this regard.

67 See report on Regulating for responsible data innovation: the role of insurance regulators in dealing

with risks relating to consumer data protection and privacy published by the Access to Insurance
Initiative (A2ii) in October 2018, available at https://www.a2ii.org/index.php/en/knowledge-center/icpself-assessment/regulating-for-responsible-data-innovation-the-role-of-insurance-regulators-indealing-with-risks-relating-to-consumer-data-protection-and-privacy .
68 See report on Effective Approaches for Financial Consumer Protection in the digital age publishedby the G20/OECD Task Force on Financial Consumer Protection (Task Force) in October 2019. The
report contains a number of overarching considerations that policy makers and supervisors should take
into account when applying financial consumer protection principles in the digital environment, including
Protection of Consumer Data and Privacy, and is available at https://www.oecd.org/finance/financialeducation/Effective-Approaches-FCP-Principles_Digital_Environment.pdf .
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Some examples of traditional and non-traditional data
sources and data types in insurance
Source: Big Data and Insurance: Implications for Innovation, Competition and Privacy,
Geneva Association, March 2018

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Reference material on impact of BigTechs, FinTechs andthird party cloud-service providersa.

Regulating and supervising the clouds: emerging prudential approaches for
insurance companies
Financial Stability Institute, FSI Insights on policy implementation No 13, December
2018

b.

BigTech and the changing structure of financial intermediation
Bank for International Settlements Working Papers No 779, April 2019c.

Big tech in finance: opportunities and risks
Bank for International Settlements, Annual Economic Report, June 2019 at page 55

d.

BigTech in finance: Market developments and potential financial stability implications
Financial Stability Board, December 2019e.

Third-party dependencies in cloud services: Considerations on financial stability
implications
Financial Stability Board, December 2019

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