Data Sharing for Fraud Detection in Insurance: Challenges and

Possibilities

Carl Christophe Louis Søilen-Knutsen and Bjørnar Tessem

Department of Information Science and Media Studies, University of Bergen, Norway

Keywords:

Data Sharing, Insurance, Fraud Detection, Innovation.

Abstract:

Digital development has opened up new tools to enable innovation, one of the options being data sharing

among businesses. This paper addresses data sharing in the insurance industry and its innovation potential

through a case study from a Norwegian data sharing project. The goal of the studied project is to achieve

cross-company data sharing and with that enabling more efﬁcient insurance fraud detection. We look at what

requirements need to be fulﬁlled for data sharing to be implemented and what kind of challenges such a data

sharing project meets. We analyse interview data from project participants and systematize their opinions and

impressions regarding possibilities and challenges for data sharing. The case shows that data sharing among

competitors in the insurance industry is hard to realise, very much due to the lack of trust in how the others

will use data, but also due to competition laws and other regulations.

1 INTRODUCTION

Data sharing has great potential in business process

innovation (Richter and Slowinski, 2019). However,

there are in general many obstacles in the way of com-

panies, entrepreneurs and the business community be-

fore they will be able to fully exploit the potential that

data sharing can offer.

The insurance industry is a particular domain

where data sharing may beneﬁt both business and cus-

tomers. A project led by the Finance Innovation busi-

ness cluster in Bergen, Norway (Finance Innovation,

2021), is currently underway adopting data sharing

among insurance companies with the aim of prevent-

ing insurance fraud. The assumption is that a larger

pool of data about customers’ coverage and claims

will improve the ability to uncover fraud. The chal-

lenges are many and the biggest challenges may not

be technical but rather at the legal level. For example,

many of the project participants indicate that Norwe-

gian legislation regarding data sharing is outdated.

Data sharing with personal data is further com-

plicated by the General Data Protection Regulation

(GDPR) within the European Union (EU), which also

applies to Norway. The GDPR makes it pertinent to

question whether the project of Finance innovation

can be implemented at all, because of its restrictions

on personal data. At the same time, the EU recognizes

that data sharing is an important instrument in the de-

velopment of the European economy (Arnaut et al.,

2018).

Several authors have addressed data sharing

among companies, and they all point to the possib-

ilities in various businesses areas (Huttunen et al.,

2019; Celtekligil and Adiguzel, 2019; Tang et al.,

2018). This also includes data sharing for the pur-

pose of insurance fraud detection (Power and Power,

2015). Others are focusing also on the difﬁculties the

companies may encounter (Eckartz et al., 2014). To

develop the idea of data sharing as a practical innova-

tion tool there has been suggested several frameworks

for organising and enabling data sharing (Grabus and

Greenberg, 2017; Richter and Slowinski, 2019; Eck-

artz et al., 2014). In this research we take a case study

approach, and address the issues of data sharing met

in Finance Innovation’s data sharing project. We use

the interview data we have gathered to further develop

our understanding of the data sharing concept, and

aim to develop a richer understanding of the problems

and opportunities related to data sharing.

We start with a background of current research

on data sharing, and continue with a description of

the Finance Innovation project ”Fra data til innsikt”

(From data to insight). We go through our data col-

lection approach, and try to draw some new insights

in a discussion section, before we conclude.

Søilen-Knutsen, C. and Tessem, B.

Data Sharing for Fraud Detection in Insurance: Challenges and Possibilities.

DOI: 10.5220/0010982300003179

In Proceedings of the 24th International Conference on Enterprise Information Systems (ICEIS 2022) - Volume 1, pages 93-99

ISBN: 978-989-758-569-2; ISSN: 2184-4992

2 DATA SHARING

Arnaut et al. (Arnaut et al., 2018) describe, in

their report to the European commission, data shar-

ing as ”The process by which a company re-uses data

from another company, which is not a direct market

competitor, for its own business purposes (excluding

contractor-subcontractor relationships). These data

were either accessed for free or acquired against some

remuneration or other kind of compensation, includ-

ing the provision of a service.” The EU report sug-

gests that such data sharing may contribute signiﬁc-

antly value to companies and society, in terms of new

business models, new products, and improved internal

efﬁciency in companies. Within a government open

data perspective, it has been shown that access to

more data gives a small, but clearly signiﬁcant in-

novation value (Jetzek et al., 2013). Even though the

data sharing concept we are referring to here is not

100% the same thing, authors repeat the message also

for business-to-business (B2B) data sharing; there are

huge gains to be found (Huttunen et al., 2019).

Power and Power state that insurers around the

world can save large sums if data can be shared in-

ternally in the industry. To achieve this, it is neces-

sary that the process be streamlined so that sharing

can take place as efﬁciently as possible and fraud can

be uncovered more easily (Power and Power, 2015).

They suggest that one could use data ranging from in-

dividual insurance claims, overall frequency of insur-

ance use, types of insurance activities, individual be-

havioral data, and individual social media activities.

Then probability based analyses of the data should

enable targeting of the suspicious cases. There has

also been parallel reﬂections about the possibilities

and challenges in health care, mentioning for example

the potential in sharing data about rare cases (Tang

et al., 2018).

Huttunen et al (Huttunen et al., 2019) did an em-

pirical study documenting B2B data sharing in Fin-

land. 49% of companies in Finland did data sharing,

and they had signiﬁcant business gains from this; in

particular from optimised operations. However, the

need for data market places was not so accentuated,

as data sharing often was established through bilat-

eral agreements. Celtekligil and Adiguzel (Celtekli-

gil and Adiguzel, 2019) has also veriﬁed innovation

value from data and information sharing in tech in-

dustry, in particular if management support is high.

A few frameworks for data sharing addressing dif-

ferent aspect have been suggested. Richter and Slow-

insky emphasise the need for data sharing platforms,

and suggest to develop a framework for exchange of

data similar to the FRAND model for exchange of

patents (Richter and Slowinski, 2019). Eckartz et al.

(Eckartz et al., 2014) is more oriented to the prac-

tical issues of data sharing and have developed a four

step framework for the process of sharing data, in

particular addressing constraints. These constraints

are Ownership, Privacy, Economic, Data Quality, and

Technical. They used three cases from logistics to il-

lustrate their model. Grabus and Greenberg (Grabus

and Greenberg, 2017) have collected a number of

data sharing agreements, identiﬁed commonalities,

and developed a framework that could be used to

automatically generate agreements taking care of pri-

vacy, legal concerns and other restrictions.

3 FROM DATA TO INSIGHT

Finance Innovation

(FI) is an initiative from the ﬁn-

ancial industry in Bergen, Norway to start a centre for

innovation in Fintech. FI was started in early 2018

and one of their initial actions was to develop a col-

lection of projects. The insurance industry signalled

an interest in sharing data, motivated by an assump-

tion that more data would give more precise mod-

els for fraud detection. This was especially true of

the companies engaged in asset insurance, e.g., the

insurance company Tryg

, which already had fraud

models, but wanted more data. But also within the

ﬁelds of people insurance (life, health, pension) there

was interest. Yearly there is uncovered fraud claims

for more than 300 million Norwegian Kroner in Nor-

way, and the insurance business actors think the un-

discovered fraud has a value many times higher than

that (Finans Norge, 2019).

Fraud models are algorithms that insurers use to

assess whether an insurance claim is legitimate or is

an attempted fraud. Such models are used by all mod-

ern asset insurance companies, and they use machine

learning and data to improve their own fraud detec-

tion. The assumption is that the more data a fraud

model has access to, the better the model will be

able to detect an insurance fraud. At the same time,

people insurers such as DNB Liv

want to develop

better computer-based models in life, health and pen-

sion insurance. Today, investigations of fraud suspi-

cions against insurance claims in people insurance are

something that is initiated based on assessments from

case ofﬁcers only.

From this the project ”Fra data til innsikt” (From

data to insight) was started with the goal of facilit-

ating the sharing of claims data and fraud data from

https://ﬁnanceinnovation.no/

https://www.tryg.no

https://www.dnb.no/forsikring/personforsikring

ICEIS 2022 - 24th International Conference on Enterprise Information Systems

the various insurance companies (Finance Innovation,

2021). The project started with participants from the

ﬁnance industry, including Tryg and DNB Liv, con-

sulting companies such as Webstep and PwC, and

researchers from the University of Bergen, the Nor-

wegian School of Economics and Business Admin-

istration and Western Norway University of Applied

Sciences. Engagement has been high at times, but at

present the activity is low. For the time being, there is

work going on with a so called minimal viable proto-

type (MVP) within car insurance, involving only two

companies.

Within the project, it immediately became clear

that there were challenges on several fronts. There

were business and legal challenges, technological

challenges, challenges with what data can be shared,

and challenges with analytical methods. Over the past

two years the project worked on the various issues,

and received strong support from the subdivision for

insurance in Finans Norge

(collaboration organisa-

tion for the Norwegian ﬁnance industry), and inside

the participating ﬁnance industry companies.

However, in the last year and longer, the project

has had little progress. Thus, the project can hardly be

said to be a success. But perhaps that particular fact

makes it more interesting to uncover what factors con-

tributed to today’s status of the project. Hopefully a

case study of the project can provide important learn-

ing and understanding to aid future innovation pro-

jects with data sharing.

Thus, through a case study (Benbasat et al., 1987)

of this project we may provide some valuable answers

to these questions: What challenges in data sharing

for fraud detection does the insurance industry have

to address? What solutions may contribute to realize

the innovation potential of data sharing for fraud de-

tection?

4 DATA REGARDING THE

PROJECT

We have gathered only qualitative data about the pro-

ject. The most important data are interviews with pro-

ject participants, but there is also some documentation

in the shape of internal project emails.

The emails used in the analysis are provided from

one of the authors who had an active role in the pro-

ject. These emails also had attached some ﬁles (texts,

presentations) relevant to the project. These emails

and the attachments were used to get an overview of

the earlier stages of the project and the direction the

https://www.ﬁnansnorge.no/en/

project took.

The main data for our analysis are semi-structured

interviews with project participants. The participants

have been selected on the basis of the second author’s

knowledge of the project and on suggestions from

Finance Innovation’s project managers. It was a goal

to have a diverse professional background in the re-

spondent group. All the requested respondents were

positive to participating in the interviews. The inter-

view guide is found in the Appendix.

We interviewed seven respondents. Of these, six

are still active in the project, while one has been

out of the project for a couple of years. Almost all

interviews were conducted digitally using Microsoft

Teams due to the risk of coronavirus. One inter-

view was conducted over the phone. Participants were

made aware that they were recorded on audio or video

recordings.

The respondents were

1. Project manager, Finance Innovation

2. Manager, tech consultancy company

3. Manager, business intelligence, insurance com-

pany

4. Data scientist, insurance company

5. CEO, Finance Innovation

6. Lawyer, consultancy company

7. Project manager (previous), Finance Innovation

5 ANALYSIS OF INTERVIEWS

AND EMAILS

In the following the ﬁndings from the interviews are

organised into seven topics:

• misaligned expectations

• GDPR and other regulations

• public support

• technical solutions

• facilitating the project

• the future of fraud

• other opportunities

During analysis, statements were coded with terms

matching topics in the interview guide, but we also

coded for other issues. Topics that emerged as im-

portant during the analysis were misaligned expecta-

tions, technical solutions, and facilitating the project.

The other four topics came out of issues addressed

directly in the interview guide. During interviews the

questions about GDPR, public support, fraud in the

Data Sharing for Fraud Detection in Insurance: Challenges and Possibilities

future, and new business opportunities got the richest

answers.

5.1 Misaligned Expectations

In the project’s earlier phases, it was difﬁcult to get

the actors to agree on the scope of the project. The

project initiative came from Finance Innovation, and

to a less extent from the participants. The earlier

meetings was meant to create some consensus, and

data sharing was from the start the main theme, and

then insurance fraud was seen as one possible arena

for data sharing.

There was a lot of unstructured discussion about

how things could be done and it was difﬁcult to reach

agreements, and the emails show that the same topics

are recurring from meeting to meeting. Respondent 2

said, ”there was a working group discussing and there

somehow never became anything real out of it”. The

various participant companies sent people with dif-

ferent background. For example, one insurance com-

pany sent a data analyst, whereas another sent a busi-

ness responsible. One aspect was the varying size of

the companies. Some are market leaders, and others

are smaller niche companies. One respondent pointed

to the lack of funding to the project from the parti-

cipating companies: ”We need money, we need ﬁn-

ancial commitment from the actors, that’s what it’s

really about”.

5.2 GDPR and Other Regulations

All respondents considered GDPR to be a challenge

to the project’s feasibility. However, it was not con-

sidered the biggest challenge among all the respond-

ents. But no one wanted to push the regulatory limit-

ations, and this meant that important parts of the data

would need to be excluded from data sharing. Re-

spondent 1 said: ”We have sacriﬁced quite a bit of

explanatory power in order to stay on the right side

of the law”. They also mentioned other issues, like

competition regulations in Norway, which state that

it is not legal for businesses to cooperate if they have

more than 30% market share. There are also laws that

restrict the sharing of information among ﬁnance in-

dustry companies.

It was stated that both the GDPR and the Nor-

wegian competition laws are unclear and that this

presents signiﬁcant challenges for the project. A sim-

ilar project has not been done earlier in Norway. This

means that the interpretation of laws lacks precedents,

further complicating the understanding of the laws

and consequently the progress of the project.

5.3 Public Support

During the interview the government’s role in the pro-

ject was discussed, and several of the respondents

mentioned Datatilsynet

(Norwegian Data Protection

Authority). They have set up a regulatory sandbox

where businesses can try out solutions without being

afraid of breaking regulations. This project would be

a good case for the sandbox, and would ensure that

all the issues with regulations could be put aside for a

while. Respondent 3 said, ”the regulatory sandboxes

are a place where you can test out a project, without

fear of reprisals, you might say. Where you work

in cooperation with the regulatory authorities.” One

of the respondents mentioned that this project has so

high societal value that it should allow for exemptions

from regulations.

5.4 Technical Solutions

Recently the project has decided to focus on creating

a MVP solution for car insurance. Only two of the

initial insurance companies are involved in this (at

the last time we checked), although one would have

wanted more to make a solution viable. The envi-

sioned, so called black box, solution is one where the

companies will be able to see their own data only. But

they may learn fraud models using all the available

data. It is assumed that this will solve the regulat-

ory challenges. However, this solution is according

to respondent 6 semi-optimal. He said: ”The solu-

tion, considering legal issues, would be to anonymise

the data sets. Then you can do whatever you want

with data and you can keep them for how long you

want. But they were not able to do that. So I think the

black box solution was a very good way to be able to

actually give that AI enough data to train a model”.

Respondent 4 was more positive, and focused on how

the MVP reduced chances of data misuse, but with

a realistic twist: ”It is quite difﬁcult to completely re-

move the ability to misuse the data and we have talked

about different solutions to that.”

5.5 Facilitating the Project

As mentioned the project was initiated from FI, as a

response to a brain storming process among ﬁnance

industry actors. And having a facilitator like FI was

essential for the project to get going. The importance

of Finance Innovation being a non-proﬁt organization

in the middle was pointed out as a trust builder among

participants. For example, using the premises of FI

www.datatilsynet.no

ICEIS 2022 - 24th International Conference on Enterprise Information Systems

allowed companies to meet on neutral ground and co-

operate. It would have been different if such a meet-

ing were to take place at one of the companies. In

particular, trust was seen as important because there

are fears that the data may be misused by dishonest

forces. One of the respondents said that the weekly

meetings and were an important contributor to creat-

ing trust.

But still the project progressed slowly. Respond-

ent 3 suggested this was due to the lack of an early

”minimum solution, good enough to demonstrate to

the managements of the various insurance compan-

ies that this is something that will bring great value.

Which will be worth investing in.” The minimal solu-

tion was hard to do because of lack of example data

from the various companies. In the end, they seemed

to not dare to share data for an experimental solution.

The companies had a strong scepticism towards shar-

ing data because of a fear that competitors might get

insights into their business models. It also was clear

that it was difﬁcult for them to set aside the people re-

sources to prepare data. This again relates to the ﬁn-

ancial commitment from the actors that we also men-

tioned previously in the analysis.

5.6 The Future of Fraud

The respondents were invited to reﬂect on what the

effect of such data sharing could be. Respondent 3

said ”There is quite a few hundred million a year

in costs so there is clearly a potential in preventing

fraud. Thus in the utmost consequence you give a

cheaper insurance to the honest customers”. But there

is no indication that such sharing of data will actu-

ally lead to more uncovered fraud. This remains to be

seen. Still, some speculated about the possibility from

data sharing to uncover new and more advanced fraud

attempts. They were sceptical about the customers’

ability to change their behavior as more fraud would

be uncovered. The arguments was that they do not

have insight into the algorithms, but also that insur-

ance fraud is mostly not done by professional crimin-

als.

5.7 Other Opportunities

Some respondents envisioned that data sharing also

could give other opportunities than improved fraud

detection. Respondent 2 reﬂected on the possibility

to compete with international actors who have a large

advantage in amounts of data, like Google and Face-

book: ”this is an opportunity for the Norwegian insur-

ance industry to meet competition with any interna-

tional players who come with a large data advantage”.

Although related to data sharing only indirectly, auto-

matizing decision processes was mention by several

respondents. But they did not see any other business

opportunities or new products.

6 A NOT SO SUCCESSFUL DATA

SHARING PROJECT

The ”From Data to Insight” project was started with a

diverse group of participants, with the aim of sharing

data among companies in insurance. The project de-

cided to focus on sharing data for the detection of in-

surance fraud, based on assumptions about a potential

similar to what Power and Power argued for (Power

and Power, 2015). That is, more and richer high qual-

ity data shared among competitors would enable auto-

mated fraud detection, and signiﬁcant reductions of

losses. The project was not successful compared to

the initial expectations, and has ended up with only

two partners sharing data, aiming towards a somewhat

restricted black box solution.

If we look at EU’s deﬁnition of data sharing (Ar-

naut et al., 2018), we notice immediately that this

does not include data sharing among competitors,

which the FI project actually aims to do. And perhaps

this is also the reason why the project never really

took off. Eckartz et al.’s (Eckartz et al., 2014) list

of constraints had Ownership on the top, and most

likely there was some lack of trust (or not enough

trust) among the companies regarding how others

would use the data to infer business models. The com-

panies actually saw a big problem in sharing these

data. This, together with the regulations imposed

by Norwegian competition laws put in effect due to

being competitors, actually created some Ownership

hurdles in the start that the project never managed to

pass. Privacy was also considered an important is-

sue, but since actually no data was really shared in

the sense that competitors could see each other data,

it was less of a problem than the competing company

issues.

Another aspect that hindered the development of

the project, was the lack of technical solutions show-

ing its viability. This most likely caused less support-

ing management in the participating companies. They

wanted to see how value could be realised before

committing to the data sharing idea. An early regulat-

ory sandbox solution demonstrating the value would

probably have helped in engaging management more.

Celteklicil and Adiguzel (Celtekligil and Adiguzel,

2019) have shown that, as in most information sys-

tem projects, management support and involvement is

critical to success. The insurance business is conser-

Data Sharing for Fraud Detection in Insurance: Challenges and Possibilities

vative, and wants to see clear value from initiatives.

Huttunen et al. (Huttunen et al., 2019) showed

that shared spaces for data sharing are not necessar-

ily needed, bilateral solutions may give just as high

value. The advantage is that those may result in less

complex agreements, and remove some of the more

challenging competition and privacy issues. Eventu-

ally this was the outcome of this project, two compan-

ies collaborating on data about car insurance.

In the initial research questions for the project we

aimed to get insights into what kind of solutions were

possible in such a project. What we observed is that

beyond the restricted black box solution, which is

not yet operative, there are basically are no solutions

found yet. Very much stopped due to the inability to

handle the initial constraint of ownership. Perhaps, if

one had a focused on a demonstration solution from

early on, there would been more engagement in trying

to solve the ownership issue.

This project is a case study with a limited set of

interview respondents, and will of course not general-

ise to other situations, and in particular to other pro-

jects on data sharing for insurance fraud detection.

However, it illustrate some of the problems such a

project may meet, and emphasise the need to focus

on early demonstration of a viable system, focus on

handling trust among data sharers, and commitment

among managers to assign the necessary people re-

sources. As mentioned, insurance fraud has large so-

cietal costs, and solutions to reduce it will have great

value for companies, customers, and society in gen-

eral.

7 CONCLUSION

This paper describes a case study of a data sharing

project in the insurance industry, where data sharing

was hoped to be a driver for innovation. The pro-

ject was in large parts unsuccessful. We have ob-

served how data sharing among competitors becomes

problematic due to several forms of regulations, di-

vergent interests, lack of trust, lack of management

support, and other issues. The case study shows that

data sharing among companies is difﬁcult to realise,

even though there is a large potential for economic

gains for all parties. One observation is that early

demonstrators of solutions, for example, in a regulat-

ory sandbox, could contribute to increased trust, man-

agement support, and engagement.

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ICEIS 2022 - 24th International Conference on Enterprise Information Systems

APPENDIX

Interview Guide

• Tell me about your professional background and

how you got into the ﬁnance innovation project?

What kind of tasks do you have on your project?

• What do you see as the biggest challenges facing

the project today?

– Do you think of GDPR as a challenge to your

project? Why? Why not?

– What challenges does the GDPR present?

– Do you think that Norwegian regulations

present challenges for the project? In case,

what challenges?

– Do market participants pose any challenges? In

case, what challenges?

• What do you think it takes for the project to be

realized?

– Do you think trust is an important factor?

– Why is trust important? How can trust be im-

proved?

– Can the insurance industry learn anything from

the banking industry in terms of data sharing?

The have managed to cooperate on systems

such as BankID. What do you think you can

learn from it? What can be transferred from the

banking industry to the insurance industry?

– Do you think this is something the Norwegian

authorities can do to realize the project? In case

what?

• What opportunities do you think the project can

give the insurance industry?

– Do you think that the insurance industry will be

more efﬁcient with data sharing?

– Do you think it can give the insurance industry

new business opportunities? In case what?

• How do you think the project could help uncover

insurance fraud?

– To what extent do you think it could help un-

cover insurance fraud?

– How will it support the process of solving in-

surance fraud?

– Are there any special types of fraud that are go-

ing to be more easily uncovered than others?

– Could we see new forms of insurance fraud if

the project is successful? In case what?

Data Sharing for Fraud Detection in Insurance: Challenges and Possibilities