Review

Can we trust consumers survey answers when dealing with insurance fraud? Evidence from an experiment#

Kerstin Puchstein*Jrg SchillerFrauke von Bieberstein

Abstract

Consumer surveys (e.g. questionnaires, telephone surveys) are an important means to measure the acceptability and willingness to commit insurance fraud as well as related influencing factors. However, for such a sensitive issue, the data validity and reliability is unclear. We use a two stage within-subject procedure which consists of an experiment and a questionnaire. In the experiment participants are incentivized and have the opportunity to commit fraud. We compare participants behavior in the experiment and their answers in the questionnaire and do not find a strong correlation between self-stated attitude towards insurance fraud and behavior in the experiment. Our results indicate that influencing factors for insurance fraud differ in their direction and significance with subject to the data collection method used.

30 October 2014

# Financial support of the German Insurance Science Foundation (Deutscher Verein fr Versiche-rungswissenschaft e.V.) is gratefully acknowledged. We thank Christian Biener und Martin Nell for helpful comments.

* University of Hohenheim, Department of Accounting and Finance, Schloss Osthof, 70593 Stuttgart, Germany, E-Mail: [email protected] (corresponding author).

University of Hohenheim, Chair in Insurance and Social Systems, Fruwirthstr. 48, 70593 Stuttgart, Germany, E-Mail: [email protected].

University of Bern, Institute for Organization and HRM, Engehaldenstr. 4, 3012 Bern, Switzerland, E-Mail: [email protected].

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1 Introduction

Insurance fraud is a serious problem in the insurance sector all over the world. For Germany, fraudulent activities are estimated to cause a damage of 4 billion EUR (Debreband, 2011). Surveys indicate that about 4% of all German households committed insurance fraud in the last five years and another 7% of respondents have information about fraudulent behavior among their acquaintances (John, 2011). In the United States the annual extent of insurance fraud is appraised to a range between 89 to 300 billion USD. According to the Insurance Research Council over one third of all bodily-injury claims of auto accidents include fraud. This implies that about 17-20 USD cents of every dollar of these claims cover consequences of insurance fraud. About 5.2 to 6.3 billion USD are added to automobile insurance premiums in the American market due to dishonest behavior (Zalma, 2011).

Insurance fraud is only possible as the occurrence and/or the specific loss size is (partly) private information of the policyholder. Current research focusses both on theoretical and empirical issues of insurance fraud.1 The two main theoretical approaches that deal with major forms of insurance fraud are costly state falsification and costly state verification. In costly state falsification models (Crocker and Morgan, 1998) policyholders can exaggerate claims by costly activities, which are unobservable for insurance companies. The extent of falsification (claim build-up) can therefore only be limited by an efficient design of the indemnity function, where small losses are overcompensated and high losses are undercompensated. The second approach of costly state verification goes back to Townsend (1979) and Picard (1996) and considers a situation, where policyholders are able to report fictitious losses and insurance companies can only limit these activities by costly audits.

An important focus of empirical papers is on identifying different factors which may influence the attitude towards committing insurance fraud. For example, Tennyson (2002) finds that there are significant differences between the attitude towards insurance fraud of women and men. Fullerton et al. (1996) identify that the tolerance of unethical behavior decreases in age. Most of these studies are based on consumer surveys like questionnaire or telephone surveys. The use of questionnaire data supposes that consumers attitude towards insurance fraud is closely related with their real behavior in insurance transactions. By comparing fraudulent death benefits in the life insurance market Colquitt and Hoyt (1997) found that (...) the publics perception of the acceptability of fraudulent claiming behavior is an important indicator of the extent of fraud committed in the state(Colquitt and Hoyt, 1997, p. 475). These results are confirmed by Cummins and Tennyson (1996) who find that a higher acceptability of fraudulent practices in automobile bodily injury liability is positively related to

1 For example, Picard (2013) and Dionne (2013) provide an excellent overview on the current state of the art.

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higher rates of liability insurance claiming in this state. Dionne et al. (2009) analyze 1,000 random claim reports of a European auto insurer which were not classified as fraudulent by regular claim adjusters. They found that 55 out of the 1,000 claims contained some indication of fraud.

There are only a few studies using actual claims when analyzing insurance fraud. For example Artis et al. (2002) confirm the finding that the tendency to defraud declines in age when analyzing actual claims. However, we are not aware of any study which is based on actual claims data that focusses on socio-demographic factors of policyholders, like gender and income. In the insurance industry, the use of computer-based expert systems for the detection of insurance fraud is very common. These systems rely both on observable claims and policyholder information. For the improvement of such systems a better understanding about reliable factors which may affect the attitude for committing fraud is important.

Although many studies use consumer surveys to assess the impact of socio-demographic factors, the validity and reliability of these surveys are unclear. Our main research question is: do consumer surveys provide a reliable and valid instrument when they are used to investigate the sensitive issue of insurance fraud? Validity is given if the design of the survey is reasonable to investigate the stated research question. A high level of validity allows the generalizability of the results.2 Validity can be categorized in different sub-categories.3 Here, the criterion validity is of relevance. A test possesses criterion validity if the relationship between the measure of the test and the measured external criterion in real world is close. Especially, for sensitive questions the use of self-reported measures can lead to difficulties of criterion validity (Thornberry and Krohn, 2000, p. 52). Reliability is given if a measure is robust over a repeated exercise and free of measurement errors. Reliability can be tested in different ways.4 Here, we use a procedure based on a parallel test. In a parallel test, the same question is investigated using two different test forms for the same set of individuals within a short period of time. A direct comparison of peoples survey responses to their actual behavior is almost impossible in an insurance fraud context. We therefore compare the actual behavior in an incentivized insurance experiment, where participants can exaggerate actual losses or can claim losses that never occurred, with their responses to standard survey questions.

2 Some authors classify these characteristics of validity as internal and external validity (see e.g. Campbell, 1957).

3 Such categories are e.g. content validity, face validity and construct validity (see e.g. Thornberry and Krohn, 2000 or Moosbrugger and Kelava 2012). 4 Other methods to test for reliability are for example test-retest reliability or internal consistency (see e.g. Thornberry and Krohn, 2000 or Moosbrugger and Kelava, 2012).

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The advantage of an economic experiment is that real people earn real money for making real decisions about abstract claims that are just as "real" as a share of General Motors (Smith, 1976, p. 274). In a lab experiment, the researcher is able to control for the decision environment. As a direct negative consequence, the generalizability is also unclear due to the artificial decision environment (Richter et al., 2014). In field experiments, decision-making is embedded in a more natural setting. Consequently, as noted by Harrison and List (2004) there might be substantial differences between field and lab experiments. Apart from the decision environment, one important difference between the two forms of experiments is usually the nature of the subjects pool. Lab experiments are mainly conducted with students, whereas field experiments often use pools of subjects with a greater variety of socio-demographic characteristics which allows controlling for biases (Blackburn et al., 1994). Naturally, there is a tendency to favor the field to the lab. However, findings of Harrison et al. (2007) with respect to risk attitudes for example do not lead to a clear superiority of field experiments. Inferring consumer preferences from actual choices (reveled preferences) is an integral part of economic theory. When it comes to illegal or sensitive activities, this general approach is often not feasible, as the extent of activity is unobservable. In these areas data from anonymous surveys might be the best source for analyzing consumers preferences. But the validity and reliability of survey data is particularly important for sensitive topics like the consumption of illegal drugs, sexual behavior, the personal health situation or criminal offences like insurance fraud. Here, results from anonymous questionnaires may be susceptible for biases.

According to Tourangeau et al. (2009) a sensitive question comprises three properties: Firstly, it is intrusive, in the sense that the topic is considered as very private information. Secondly, there are social norms pertaining to the question. Therefore, respondents might opt for a socially desirable reply or give responses which they consider as moderate. Thirdly, there is a threat of disclosure if the reply was known by a third party.

Insurance fraud is a criminal offence and socially not accepted. The question of supporting or doing illegal activities could be classified as a private issue. Survey respondents who are declaring in doing so might become scared of the consequences. Regarding the second criterion, different fraud types have to be distinguished. While claim build-up is often considered as common practice and a weak form of insurance fraud, reporting fictitious claims is contemplated as a stronger form of fraud for which a higher level of criminal energy is necessary (Psychonomics, 1996). Thus, social desirability might play a stronger role when it comes to fictitious claims. Overall, since all three properties apply, we consider the question of accepting or practicing insurance fraud in surveys as a sensitive one for which a

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higher level of carefulness in analyzing and interpreting is given. Therefore, the truthfulness of replies must at least be put into question.

Several studies indicate that misreporting in surveys is quite common for sensitive topics. Tourangeau and Yan (2007) describe three possible distortions: The number of participants of the whole survey could decrease if they expect sensitive questions to be asked. The number of respondents of a certain, sensitive question could decline and the response accuracy could be reduced. In our analysis, we will focus on the last category. Since the respondent had no possibility to skip certain questions or to jump over the whole questionnaire, this is the only relevant category in our experimental analysis. According to Tourangeau and Yan (2007) there exists a systematic underreporting for socially unaccepted forms of behavior and a systematic overreporting for socially accepted kinds of behavior. For example this is confirmed by the results of Heeb and Gmel (2005), who are investigating self-reported consumption of alcoholic drinks or Fendrich and Vaughn (1994) who analyze the effect of underreporting in the consumption of cocaine and marijuana in a survey.

Since insurance fraud is a criminal act and its practice is socially not respectable, it is straight forward to presume that individuals self-expression in consumer surveys differs substantially from their actual attitude and behavior. The fact that individuals might deliberately misreport their attitude with respect to fraudulent activities could cause a serious distortion of survey results. By combining common questionnaire answers with actual behavior in an incentivized economic experiment, we try to evaluate how reliable and valid surveys about consumers attitude towards insurance fraud are. Of course, our analysis can only offer an indirect test and we cannot evaluate the reliability and validity of survey answers concerning insurance fraud. However, linking survey responses of individuals with their behavior in an experiment is in our view a promising first step.

In the experiment participants make decisions in an insurance setting. Losses are private information. Therefore, participants can honestly report their claims, inflate their loss size (claim build-up) or can report losses that have never occurred (fictitious claims). After the experiment, participants answer typical survey questions querying individuals socio-demographic characteristics as well as attitudes and experiences towards insurance fraud. We compare individuals responses in the questionnaire with their actual behavior in the experiment.

We find that participants behavior in the experiment does not always correspond with their stated attitude towards insurance fraud in the questionnaire. Conformity of individuals behavior in the two data sources is only available for approximately half of participants. There are also differences between the investigated fraud types. We therefore separate two

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criteria when we identify influencing factors on insurance fraud: the type of fraud and the method of data collection (experiment/questionnaire). Depending on the method of data collection, different impact factors are significant. Our findings reinforce doubts on the reliability and validity of survey responses, as there is no strong relationship between individuals acceptance of insurance fraud in the questionnaire and their behavior in the experiment.

Given that participants first make decisions in the experiment and then answer the questionnaire, answers in the questionnaire could be influenced by actual behavior in the experiment. In particular, the theory of cognitive dissonance (Festinger, 1962) suggests that people try to avoid a discrepancy between their stated values and their actions. Note that if this effect was present in our study, we would underestimate the difference between questionnaire replies and experimental actions. In this respect, our findings can be seen as a conservative estimate of these differences.

The paper is organized as follows: Following the introduction, Section 2 presents a literature overview of recent empirical literature of influencing factors for insurance fraud. Section 3 provides a brief overview of the design and proceedings of the analyzed experiment. Section 4 is split in two parts. The first part descriptively matches the data generated out of the experiment with the data out of the questionnaire. In the second part of the section the results of our econometric model are presented. Section 5 concludes.

2 Literature Overview

2.1 Socio-demographic and Environmental Factors

The attitude towards insurance fraud is strongly influenced by the social and cultural environment as well as other socio-demographic factors. Fullerton et al. (1996) investigate consumers judgment of ethically disputable actions in several situations using a questionnaire. Their analysis indicates that with an increase in age, the tolerance for unethical behavior declines. By investigating automobile claims from accidents in Spain Artis et al. (2002) find that younger individuals have a significantly higher fraud probability. Another interesting finding is that a higher level of education and a higher income are related to a greater acceptance of unethical behavior. Tennyson (2002) builds different income categories and shows that individuals in the highest income category are less likely to accept fraud than those of lower income categories. According to a study of Psychonomics (1996) the satisfaction with the personal financial situation is more important than income. It turns

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out that individuals who are less content with their status quo have a higher affinity to insurance fraud.

Another common finding in the literature are gender differences with respect to social issues and risk bearing. Males are often more willing to accept fraudulent behavior than females (Tennyson, 2002). Weeks et al. (1999) find significant differences in ethical judgment between women and men. In a broader sense, women are more risk averse than men (e.g. Croson and Gneezy, 2009). Several other studies have shown that the neighborhood and the living area have an effect on individual behavior (Elliot et al., 1996). This implies that people also learn and adapt their behavior from their environment. If among their acquaintances or in their neighborhood insurance fraud is approvable and common, it is more likely that individuals follow this attitude (Tennyson, 1997). Tennyson (2002) also analyzes the impact of residential areas and shows that suburban residents are less willing to agree with insurance fraud than urban residents.

In our analysis we control for gender effects and risk aversion. However, as participants are mainly students from the area of Munich, we do not control for age, income and residential area.

2.2 Experience and Perception towards Insurance Companies

In general, a limited understanding of insurance mechanisms may result in mistrust and a negative perception of the insurance industry. However, the insurance industry may also not sufficiently support customers education for a better understanding of their products. Both factors may lead to an unappealing environment encouraging fraudulent behavior (Hoyt, 1990). Using data of an US survey of 1,987 individuals, Tennyson (1997) explores the relationship between the attitude towards claim build-up in automobile insurance claims and the perception of insurance companies. Individuals who are less confident with the financial stability of their insurer tend more towards a fraud supporting attitude. Those who consider their premiums as a financial burden have a higher acceptability of recouping deductibles but not of recouping their premiums paid.

Tennyson (2002) tests whether or not there is a systematic relationship between insurance experience and the acceptability of deception. Experience is defined as the number and type of policies held as well as the type of losses claimed in the past two years. She found that the number of types of insurance contracts is not significantly associated with the attitude towards insurance fraud. But, participants with claiming experience in recent years have a lower level of acceptability of fraudulent behavior. One possible explanation is that through

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the process of claims settlement and the communication between the two parties, the insurer signals deception as not inacceptable. Using automobile claims data from a Swiss insurance company, Mller (2013) examines policyholder characteristics, vehicle and policy specifications as well as loss characteristics as potential fraud drivers. In the final regression model, she finds that a lower number of previous records leads to a higher fraud probability. Contrary, by analyzing Spanish automobile claims Artis et al. (1999) show that individuals with more claiming experience have higher tendency to defraud. Experience is thereby also measured by the number of previous claims.

Based on the findings in the literature, we include four variables into our analysis: the number of insurance contracts, recent claiming experience, the individuals general attitude and perception towards the insurance industry, as well as the individuals satisfaction with his or her actual insurance contracts and the claims settlement process.

2.3 Different Contract Settings

Moreno et al. (2006) show that bonus-malus contracts may be an effective means to prevent insurance fraud. Gabaldon et al. (2009) analyze the impact of bonus-malus contracts on fraudulent claiming behavior in an experiment. They only find a mild tempering effect of bonus-malus contracts in their experiments.

Deductible contracts are often perceived as unfair and may therefore trigger fraudulent behavior. The fact that a higher deductible causes a lower premium is thereby often neglected by the policyholders (Miyazaki, 2009). Dionne and Gagne (2001) investigate claim build-up in deductible contracts using filed claims after automobile accidents. They find that the amount of the deductible is a significant determinant for the reported loss, at least when no other vehicle is involved in the accident. (Dionne and Gagne, 2001, p. 290). Using a questionnaire, Miyazaki (2009) investigates whether higher deductibles cause a greater sense of justification for fraudulent behavior and if this feeling ends in applying lower ethical standards. The results show that in case of high deductibles claim build-up will be considered as less unethical. These effects are stronger for individuals with lower ethical standards as for respondents with stricter ethical standards. In their experiment that is also the basis for the current paper, von Bieberstein and Schiller (2014) find that people are more likely to commit fraud given a deductible contract compared to a bonus-malus contract.

As the perception of the different contract forms is varying in our analysis, we control for differences in the contract settings. Table 1 provides a brief overview of important influencing factors on insurance fraud which are discussed above.

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Socio-demographic factors

variableauthorInfluence

Fullerton et al.(1996)Unethical behavior declines with an

ageincrease in age.

Artis et al. (2002)Younger individuals have a higher fraud

probability.

Fullerton et al. (1996)The acceptance for unethical behavior

increases with income.

Tennyson (2002)High income individuals are less likely to

incomeaccept insurance fraud.

Income is not relevant. More relevant is

Psychonomics (1996)the satisfaction with the individual financial

situation.

Croson and Gneezy (2009)Women are more risk averse than men.

Tennyson (2002)Men are more willing to accept fraudulent

genderbehavior.

Weeks et al. (1999)There are differences in ethical judgment

between women and men.

Elliot et al. (1996)The living area influences individuals

residentialbehavior.

The surrounding field has an impact on

area

Tennyson (1997)individuals attitude towards insurance

fraud.

Experienceand perception towards the insurance industry

variableauthorinfluence

perceptionTennyson (1997)A negative perception increases the

tendency to cheat in insurance contracts.

Tennyson (2002)Recent claiming experience lowers the

acceptability of insurance fraud.

experienceArtis et al. (1999)A higher number of records is associated

with a higher fraud probability.

Mller (2013)The lower the number of previous records,

the higher is the tendency to defraud.

Contract settings

variableauthorinfluence

bonus-Gabalon et al. (2009)Previous experience with bonus-malus

maluscontracts reduces the fraud probability.

Dionne and Gagne (2001)The level of the deductible is a significant

deductiblesfactor for the submitted claim.

Miyazaki (2009)For higher deductibles individuals consider

claim build-up as more justified.

bonus-von Bieberstein and SchillerIndividuals commit more fraud given a

malus vs.deductible contract compared to a payoff-

(2014)

deductiblesequivalent bonus-malus contract.

Table 1: Overview of influencing factors on insurance fraud

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3 Experimental Design

The experiment was conducted at the MELESSA laboratory at the Ludwig-Maximilians-University in Munich. Six treatments took place from March to July 2009. Two further treatments were done in July 2012. Differences between the first and the second data collection were group size and transaction costs for filing claims. In the first set of experiments 4 participants belonged to one group and transaction costs were 40 percent. In the second set of experiments the group size was 24 participants and transactions costs were 20 percent. The second set of experiments was designed as a control with higher fraud incentives due to a larger group size and lower transactions costs. In order to account for the differences between treatments, we control for the specific contract type and the group size in our regression analysis. The total database consists of 576 participants with 72 participants in each treatment. Almost all participants were students with various majors, but there is no predominant major.

The experiment is based on the idea of collective risk sharing. It consists of two computer-based parts. In the experiment subjects are randomly and anonymously allocated into fixed groups of four or twenty-four. For every group, a specific insurance account from which all incoming and outgoing payments are made is introduced. All payments from and to the group members are settled via this account. At the end of the game the insurance account is automatically balanced and all profits and losses in this account are equally shared between the group members. Each group plays five periods of the following insurance game: At thebeginning of each period every participant obtains a period endowment.5 Participants

are informed that they have to insure against the risk to suffer no loss( ), a small loss

or a large loss. The losses occur in each periodwith a0 probability of

(= 0)

andThe specific losses are independently determined for each group0

( = 10)( . = 15)=

memberin each period.Insurance is mandatory in the experiment. Thus, each group

0.7,= 0.2= 0.1

member in each period must pay an insurance premium to the group-specific insurance account that finances all indemnities paid to the group members. Hence, in our experiment,we apply a mutual insurance setup. In order to have sufficient data, we used the strategy

method. By choosing their claiming strategy0with0in

decisions about how muchto claim from the

each period, participants make contingent= ( (), (), ( )) ( ,,)

group-account depending on the loss realization. This means, participants make their

decision about the level of the indemnity for all=possible0,=10losses before=15 the actual loss is known. For each loss only the indemnities 0 and could be selected.

Hence, participants were able to inflate the size of the loss by claiming an indemnity that is

5 During the experiment the players are dealing in points, which will be converted by 1 point equal to 10 Euro-cents afterwards.

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higher than the loss (claim build-up) or by claiming a positive indemnity when no loss occurred (fictitious claims). In the next step the players obtain information about their actual loss occurred in this period, the indemnity they reported and their preliminary payment, without considering the group insurance account. In accordance with the chosen strategy, the indemnity will be paid virtually. After the last period, they receive their preliminary final result over all rounds. While indemnities are paid as claimed, the group insurance account is debited with the amount of the indemnity plus an additional 40 % respectively 20 % for transaction costs.

One important aspect of the experimental setup is that there is no monitoring and no sanctions for fraudulent behavior. The reason for this is that we wanted to concentrate on the preferences of the participants and not on strategic aspects or risk aversion related to costly monitoring and sanctions. To analyze and compare different features, the whole experiment consists of six different treatments. For the Base Treatment, as described above the

endowment isfor each period and the possible losses aretheand. The

is, which equals the expected value ofpossible losseswith

period premium = 25= 10= 15

transaction costs.Additionally there are three modifications of the Base Treatment. The first

= 5

type does not use insurance specific wording e.g. the groups insurance account is named group account (No Insurance Wording Treatment). In the second modified treatment again no insurance specific wording is used and in addition no transaction costs are comprised (No Transaction Costs Treatment). In the third modification the participants get information about the amount of the insurance account and its changes at the end of each period (Feedback Treatment). Since the selectable indemnity corresponds to the loss sizes, the Base Treatment depicts full insurance.

Furthermore, two treatments without full insurance are applied. In the Deductible Treatment a

deductible of 5 is introduced by increasing the amount of the possible lossesand

,while theclaimable indemnityremains unchanged. Hence, theendowmentis

= 15

increasedto, whereas the premium is. With the Bonus-Malus Treatment an

= 20

rating is introduced. Thereby the premium is not a fixed amount, it depends on

experience= 27= 5

the indemnity of the previous periods. In the first period it starts with. In the following

periodthe premiumincreases by 2 points in case anindemnitywas received in

period+1= 5

and decreases by

1 point in case no indemnity was paid. The newstarting point for

+ 1

the nextperiodis the premium in. The Deductible Treatment and the Bonus-Malus

been applied in the first and second round of data collection. 6

Treatment have + 2+ 1

6 For more details, please see von Bieberstein and Schiller (2014) and Lammers and Schiller (2010).11

The second part is based on a questionnaire about the individuals characteristics and attitudes. The questionnaire provides information about participants attitudes concerning the general compliance of rules or laws, experience with insurance contracts and companies, as well as insurance fraud. The aim of the experiment is to match behavior in the experiment with the participants survey responses. For their attendance the participants received a fixed show-up fee of 4 EUR as well as additional payments which depended on their result in the first part. Average earnings ranged from 12.85 to 15.01 EUR including the show-up fee. Answers in the questionnaire did not affect the final payment. Each session took about 50-60 minutes.

4 Results

4.1 Behavior versus stated attitudes

In our analysis, we distinguish different categories of insurance fraud. In the experimental part, participants have the possibility to cheat in two different ways: By making fictitious claims when they do not face a loss, or by claim build-up, when they exaggerate a given loss size. Participants report their claims for all possible loss situations before they know the real loss realization (strategy method). All (actual and hypothetical) contingent decisions are included in our analysis. Table 2 displays the frequencies of fraudulent behavior for the five rounds of the experiment, separated by reporting fictitious claims and claim build-up. Since the distributions of fictitious and inflated claims differ, we distinguish both types of fraudulent behavior.

Number of defraudsFictitious claimsClaim build-up

022.40 %33.33 %

112.85 %11.28 %

210.42 %7.47 %

312.33 %7.29 %

49.55 %7.29 %

532.47 %33.33 %

Table 2: Number and share of defrauds in the experiment

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Along the lines of the experiment, the questionnaire entails questions whether or not individuals in general accept fraudulent behavior or could imagine reporting wrong information to their insurance company.7 These questions separately focus on reporting fictitious claims, claim build-up, redefinition of claims and insurance fraud in general. The answers are given on a seven-point Likert-scale, whereby zero corresponds to no agreement and six implies full agreement with the respective statement or fraud type. The middle position three indicates a neutral position to the written statement. In our analysis, we divide responses to these questions into three categories. Answers on the scale from zero to two are classified into the first group as individuals who do not accept that behavior. Answers on the scale from four to six are sorted into the second group containing participants who accept this behavior. Individuals whose answer is three are categorized as neutral, as they neither agree nor disagree with insurance fraud. Figure 1 shows the distribution of the answers in the questionnaire.

Fictitious claims

66.67%

60,00%

40,00%

19.27%20,00%6.08% 4.17%2.43% 1.22% 0.17%0,00%

DisagreementAgreement

92.02 %3.82%

In general, I can imagine to report a claim that never occurred to receive an indemnity from the insurance company.

Claims exaggeration

60,00%

40,00%24.83%

20,00%15.80%16.67%11.98

%14.58%

10.94%5.21%

0,00%


43.41 %44.62%

In general, I can imagine to slightly exaggerate claims in case a loss occurred.

Redefinition of claims

60,00%

40,00%27.26%21.18%10.24

20,00%15.63%

11.28%%9.55%4.86%

0,00%

0123456


59.72 %30.04%

In general, in case a loss occurred, I can imagine to report wrong information regarding the course of events towards the insurance company to receive an indemnity at all.

Insurance fraud in general

60,00%

40,00% 33.33% 32.99%20,00%14.06% 7.99%8.51%

0,00%2.60% 0.52%

0123456


80.38 %11.63%

In general, it is acceptable to report wrong information to the insurer.

Figure 1: Fraud in the questionnaire

7 The written questions can be found in Appendix 1.

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Figure 1 indicates that the acceptance of fictitious claims is very low. Only 3.82% could imagine reporting claims that never happened, while 92.02% disagree with reporting fictitious claims. 4.17% of the individuals are neutral in this question. The distribution for claim build-up strongly differs from the previous fraud type. Here, the percentages of agreement and disagreement are almost equal. While 43.41% could not imagine to build-up claims, 44.62% accept claim build-up. The discrepancy between individuals who agree and individuals who disagree with redefinition of claims is not as high as for reporting fictitious claims, but the distribution is also not as equally spread as in case of claim build-up. While 30.04% approve redefinition of claims, 59.72% do not accept this fraud type. For fraud in general 80.38% disagree and only 11.63% accept this kind of behavior.

A comparison of the distributions shows that for fictitious claims and fraud in general the rate of disagreement is very high, whereas the rate is much lower for claim build-up and redefinition of claims. Thus, respondents seem to consider the latter two activities as less severe than giving wrong information to the insurer in general. These distributions are in line with findings in the literature, stating that claim build-up and redefinition of claims is often considered as a more acceptable form of insurance fraud since an actual loss occurred (Nell, 1998). Since we cannot measure redefinition in the experimental part, we will not focus on this type of fraud in the following.

To investigate whether we can trust consumers answers in questionnaires concerning insurance fraud, we first compare participants behavior in the experiment and their stated behavior in the questionnaire. Table 3 to Table 5 display the relation between answers in the questionnaire and behavior in the experiment. For the questionnaire we use the categorization described above: Answers from zero to two are assigned to disagreement with fraud while answers from four to six are classified as agreement with fraud. Individuals whose answer in the questionnaire is three are classified as neutral. They are not included in the following comparison. The behavior in the experiment refers to all five rounds of the experiment. Again, we build two categories: participants who cheat in zero to two out of the five rounds of the experiment are considered as individuals who disagree with fraudulent behavior. Participants who commit any fraudulent claim in more than two rounds of the experiment are classified as individuals who agree with insurance fraud. We choose this segmentation since it forms two equal groups (with each three scale items) and therefore best represents the separation made in the questionnaire.8

8 An alternative segmentation for the frequency of defrauds in the experimental part would be to classify individuals behavior as non-fraudulent if they do not report any fraudulent claim in all periods of the experiment. If an individual cheats in one or more periods, its behavior is classified as fraudulent in the alternative segmentation. Thereby, no separation of the number of periods in which the

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Fictitious claims

Disagree with fraud in theAgree with fraud in the

experimentexperiment

Disagree with fraud in the44.92 %51.09 %

questionnaire(248)(282)

Agree with fraud in the1.09 %2.90 %


Table 3: Match of fictitious claims in experiment and questionnaire (n=552)9

Focusing on fictitious claims (Table 3), 44.92% of the participants state to disagree with reporting fictitious claims in the questionnaire and cheat in two rounds of the experiment at maximum. Only 2.90% state that they could imagine reporting fictitious claims and confirm their attitude in the experimental part. With 51.09% more than half of the individuals disagree with reporting fictitious claims in the questionnaire but they do so in the experiment. Therefore, it is straight forward to assume that there exists a difference in the expressed behavior for a sensitive topic like insurance fraud in questionnaires and their real actions in experiments. A McNemars chi-square statistic indicates a statistically significant difference in the proportions at a 1 percent level.

The numbers change when claim build-up in Table 4 is considered. Thereby 26.04% disagree with fraud in the questionnaire as well as in the experiment. 24.46% of the participants agree with claim build-up in the questionnaire and do so in the experiment. Here, 23.27% state that they do not agree with claim build-up in the questionnaire but exaggerate claims in more than two rounds of the experiment. Contrary 26.23% accept fraud in the questionnaire but behave honestly in the experiment. A McNemars chi-square statistic indicates no statistically significant difference.

Claim build-up







Table 4: Match of claim build-up in experiment and questionnaire (n=507)

individual cheats is made. The tables in which the alternative segmentation is used are provided in Appendix 2.

9 Table 3 includes observations of 552 individuals. 24 observations are not included because their attitude concerning fictitious claims is categorized as neutral in the questionnaire. The number of observations for each category is stated in parenthesis below the percentages.15

Table 5 shows the results for fraud in general10. 34.34% of all participants disagree with fraud in the questionnaire and defraud maximum twice in the experiment, while 9.25% agree with fraud in the questionnaire and defraud in the experiment. 53.02% of the individuals cheat in three or more periods of the experiment but state not to accept fraudulent behavior in general in the questionnaire. The contrary case does only apply for 3.40% of the participants. A McNemars chi-square statistic indicates a statistically significant difference in the proportions at a 1 percent level.

Fraud in general







Table 5: Match of fraud in general in experiment and questionnaire (n=530)

For all three fraud types the Tables 3-5 show, that only approximately half of the participants behave consistently in the questionnaire and in the experiment. In total there exists a positive match for 47.82% of the individuals for fictitious claims, for 50.50% of the individuals for claim build-up and for 43.59% for fraud in general. The aim of the experiment is to gain information about individuals attitude towards insurance fraud and their moral concept of value. Therefore no sanctions have been included into the experimental setup. Of course, the absence of sanctions could impact individuals behavior. However, there are also no sanctions for the agreement with insurance fraud in the questionnaire. Conformity between the attitude towards insurance fraud in the questionnaire and in the experiment should therefore be even stronger in absence of any sanctions. A comparison of the three fraud types shows that when analyzing insurance fraud, the single types have to be considered separately since they are perceived differently. The range for individuals who disagree with fraud in the questionnaire and in the experiment lies between 26.04% - 44.92%. The fraction of participants who agree with fraud in both parts is low for fictitious claims but much higher for claim build-up.

To provide an assessment of the reliability and validity of questionnaire based surveys on insurance fraud, the category disagree with fraud in the questionnaire / agree with fraud in the experiment is a matter of particular interest. This category covers observations where a negative answer is given in the questionnaire that does not coincide with the behavior in the experimental part. A comparison of fictitious claims and claim build-up indicates that in case

10 Fraud in general is defined as either reporting fictitious claims or exaggerating claims or both types of fraud in one round of the experiment.

16

of fictitious claims the share of people who fall in this category is nearly twice as high as for claim build-up. Of all individuals who disagree with fictitious claims in the questionnaire, 46.79% disagree in the experiment, too and 53.21% agree. Likewise of all participants who disagree with claim build-up in the questionnaire, 52.80% accept this fraud type in the experiment while 47.20% do not. Given our findings, trusting results out of the questionnaire without further considerations may lead to misleading conclusions.

Differences between fraud types may be due to differences in the level of sensitivity of fictitious claims and claim build-up. As defined above, a sensitive question is characterized by the level of intrusiveness, of social desirability and of threat of disclosure. Since questions about both types of fraudulent behavior are classified as very private information, there is no difference in the level of intrusiveness. About the threat of disclosure, no concrete statement could be made since the punishment also depends on the amount of fraud and not only on the type of insurance fraud. Considering the criterion of social desirability, prior research indicates that fictitious claims could be more socially undesirable than claim build-up (Psychonomics, 1996). The difference in social desirability could be one possible explanation for the diverging results for both fraud types. Another possible explanation is that filing fictitious claims in the experiment was relatively easy, while it takes considerable criminal effort in the real world where a loss situation has to be fabricated. Therefore, the experiment may not represent differences in both fraud types in the same magnitude than they exist in the questionnaire. The correlation matrix in Table 6 shows that there exists a significant correlation of 0.6002 between fictitious claims and claim build-up within the experimental part. Contrary the correlation of the attitude towards both fraud types within the questionnaire is 0.1992. The discrepancies in these two correlation coefficients suggest that the fraud types are perceived differently in both measurement methods. Based on the correlation coefficients the perceived differences between fictitious claims and claim build-up are weaker when fraud is generated out of experimental data than in case fraud is generated out of the questionnaire.

17

FictitiousClaim build-Fraud inFictitiousClaim build-Fraud in

claimsupgeneralclaimsgeneral

up (experi-

(question-(question-(question-(experi-(experi-

ment)

naire)naire)naire)ment)ment)

Fictitious

claims1.0000

(questionnaire)

Claim build-up0.1992 ***1.0000

(questionnaire)

Fraud in

general0.1873 ***0.3646 ***1.0000

(questionnaire)

Fictitious

claims0.0766 *0.04440.1079 **1.0000

(experiment)

Claim build-up0.00890.01050.0856 **0.6002 ***1.0000

(experiment)

Fraud in

general0.04520.01890.0853 **0.8482 ***0.7457 ***1.0000

(experiment)

Note: The table shows the pairwise correlation coefficients. The variables used are categorized in line with the categorization above in this chapter. Significance levels p: ***

p < 0.01; ** p < 0.05; and * p < 0.1.

Table 6: Correlation matrix of fraud in the experiment and the questionnaire

Furthermore, Table 6 shows the relationship between behavior in the experiment and stated attitude towards insurance fraud in the questionnaire. The correlation between the answers in the questionnaire and the behavior in the experiment is quite low. With a weak significant correlation coefficient of 0.0766 for fictitious claims and an insignificant coefficient of 0.0105 for claim build-up in the questionnaire and in the experiment, the results are nearly uncorrelated. For fraud in general the coefficient shows a significant but low correlation of 0.0853.

The above results show that stating not to accept insurance fraud in a questionnaire does not necessarily imply an according action in an experiment. There is no clear favorability of one data set. One could argue that the experimental data may lead to more realistic results, as real actions are practiced and monetary incentives are set. The disadvantages of questionnaires, due to the particularities of sensitive topics, may increase the favorability of the use of experimental data to investigate insurance fraud. However, different perception of the distinction between claim build-up and fictitious claims may also be explained by potential problems to adequately represent the different fraud types in the experiment.

18

4.2 Influencing factors

The results above show that the expressed behavior in the questionnaire does not always coincide with the behavior in the experimental part. Fraud detection systems and prevention activities of insurance companies are partly based on policyholders characteristics, as they might be correlated with the tendency to defraud (see e.g. Schiller, 2006). The relevant policyholders characteristics are often adapted from questionnaire-based surveys. However, if they do not coincide with real behavior, detection systems may lead to wrong conclusions. Based on prior research, we therefore compare the impact of well-established influencing factors on insurance fraud by applying two different methods to measure insurance fraud. On one hand, fraud is generated out of questionnaire responses. On the other hand, the behavior in the experiment is used to investigate influencing factors on insurance fraud. Accordingly, two regression models are developed. In the first model fraud is concerned with the stated attitudes in the questionnaire and the second model measures the fraudulent behavior in the experiment. Both models use the same set of influencing factors as independent variables.

To further explore the relation between stated attitudes in the questionnaire and behavior in the experiment, we consider a third model. Here, we use fraud out of the experiment as a dependent variable and treat the stated attitude towards insurance fraud as an independent variable in addition to the influencing factors. To account for differences between fraud types, we estimate three different regressions for fictitious claims and claim build-up.

(/= 1) = ( 0 + 1+ 2 +

3 + 4 + 5 + 6 +

7 + 8 + 9 + 10 + )

In the following we present results for six different logistic regressions. The models 1a, 1b

and 1c considerfictitious claims. Model1ais relatedto thestatedattitude in the

questionnaire, whereas Model 1b measures the behavior in the experiment. In Model 1c, we further incorporate the impact of the stated attitude in the questionnaire on the behavior in the experiment. The models 2a, 2b and 2c measure claim build-up in the same manner as

the first three models. We report the results for fraud in general in Appendix 4. The estimated

models can be summarized as follows:

(= 1) = ( 0 + 1 + 2 +

3+ 4 + 5 + 6 +

7 + 8 + 9 + 10 + 11 + )

19

The explanatory variables are chosen in line with results of existing literature described in Section 3. The variable measures the impact of an individuals satisfaction

on the tendency to defraud. The variable is calculated out of two questions concerning the

satisfaction with insurance contracts in general if existing, and the satisfaction with prior claim settlement processes if experienced. Therefore the variable is generated as a binary variable which is equal to 1 in case the participant is not satisfied with its insurances

contracts in general or with the claims settlement process in case she already claimed a loss.

The variable equals 0 if no negative experience with existing insurance contracts was reported.11 The variable is related to the fact whether participants have a

negative attitude towards the insurance industry in general. The variable is the arithmetic mean of answers to two questions concerning the attitude towards insurance companies in general. The scale of the coefficient remains on a seven point Likert-scale. The variables

andmeasure the participants experience with insurance relationships.

The variablecorresponds to the number of participants insurance contracts.

coefficientis equal to 1 if the respondent has reported a claim in the last two

The

in case of absence of any claims. The explanatory dummy variable

years and is 0accounts for

measures gender differences (1 = female). Furthermore, the variable

differences in the participants stated risk attitude. The variable is alsomeasured on a seven

point Likert-scale.

Finally, we included four additional explanatory variables due to different modes in the experimental part. Existing studies (e.g. Frey and Meier, 2004, and Frank and Schulze, 2000) provide evidence that in experimental settings the behavior of individuals with a major in economics or business differs from the behavior of individuals with other majors. The dummy

variableis 1 for participants with a major in economics or business and 0 otherwise.

The dummy variableis equal to 1 for all individuals belonging to experiment

treatments with the larger group size of 24 participants (instead of 4) and reduced transaction costs. As the specific contract form in the experiment may influence behavior, we also control

for two contract types. The dummy variableequals 1 for all participants of treatments

with bonus-malus contracts. Respectively,the dummy variableis equal to 1 for all

reference group in this category is a

participants of with deductible insurance contracts. The

full coverage contract. All used questions can be found in detail in Appendix 3. Table 7 provides a summary statistics of all used variables.

11 The detailed questions used can be found in Appendix 3. The answers of these questions are given on a seven score Likert-scale. In line with the categorization of the previous chapters, we assign answers from zero to two as not satisfied with insurance contracts or with the claims settlement process. For all other answers we assume that no negative experience with insurance contracts exists.

20

VariableObservationsMeanStandardMinMax

Deviation

Fictitious Claims (Questionnaire)5520.0400.19601

Claim build-up (Questionnaire)5070.5070.50001

Fraud in general (Questionnaire)5300.1260.33301

Fictitious Claims (Experiment)5760.5430.49901

Claim build-up (Experiment)5760.4790.50001

Fraud in general (Experiment)5760.6230.48501

Satisfaction5760.1680.37501

Satisfaction general3533.8471.33506

Satisfaction claims2693.9111.75106

Negative view5763.0271.38006

Complicated contracts5763.8301.69606

Earnings5762.2241.67606

Claims5760.4430.49701

Recent claims5760.7080.86002

Contracts5761.0991.21506

Female5760.5990.49101

Risk5762.7851.47806

Major5760.1420.35001

Group5760.2500.43301

Boma5760.2500.43301

Deduct5760.2500.43301

Table 7: Summary Statistics

Table 8 summarizes the regression results for fictitious claims. The only consistent and significant impact over all three models is related to the stated risk. Therefore, participants who stated to be less risk-averse both state a higher acceptance of filing fictitious claims in the questionnaire and actually have a higher tendency to commit this type of fraud in the experiment. A more negative view on the insurance industry in general only positively affects the stated acceptance of fictitious claims in the questionnaire but did not have any significant impact in the experiment. Surprisingly, we could not find any significant gender effect for the questionnaire. Only in the experiment, we find a weakly significant effect of women filing fewer fictitious claims than men. We also find significant effects for the control variables in the experiment. Business and economics majors defraud more often in the experiment. In this part, in the larger groups with the lower level of transaction costs fraud is more prevalent. Bonus-malus contracts lead to a lower defraud rate, as a fictitious claim increases future premiums which decreases the incentive to defraud.

21

As a robustness check, we run Model 1a and Model 1b a second time with a reduced number of participants. According to our descriptive analysis we thereby excluded individuals who state to agree with fraud in the questionnaire, but do not cheat in the experimental part. Due to their divergent behavior in both parts there might be a methodological issue that these participants have problems in understanding and competing the experiment. The fullregression tables can be found in Appendix 5. For the independent variables

major difference in

andthere is no,

the direction and significance of the investigated effect. We now find a weak significant and

,,,

positive impactofrecent claimingexperienceandinthe questionnaire. Additionally

there are minordifferences in thedirectionlevel ofsignificance for some control

()

variables.

Finally, when treating the stated attitude toward fictitious claims as an independent variable (Model 1c), the latter is insignificant. Therefore, the stated attitude does not significantly affect the behavior in the experiment. This finding is in line with our analysis in the previous section. For this model, we do not provide the same robustness check as for Model 1a and Model 1c. In the check described above we exclude all participants who agree with fraud in the questionnaire but disagree in the experiment. After the exclusion, all individuals who do not accept fraud in the questionnaire do also not accept fraud in the experiment. Therefore the predicted fraud probability in the experiment for has to be one for this category (one-way causation).

22

dependent variable / model

Model 1aModel 1bModel 1c

QuestionnaireExperimentExperiment

FICTITOUS CLAIMS--0.5446891

(QUESTIONNAIRE)(0.5258146)

SATISFACTION-0.60110280.12745110.1379444

(0.6312042)(0.25801)(0.258641)

NEGATIVE VIEW0.6406695***0.0085749-0.0020064

(0.185677)(0.0681258)(0.0690032)

CLAIMS0.5137144-0.2237958-0.2354098

(0.4922188)(0.1948811)(0.1954144)

CONTRACTS0.1839095-0.0984059-0.1020047

(0.1816373)(0.0755897)(0.0757705)

FEMALE-0.7981881-0.3599954*-0.3490582*

(0.4938245)(0.1924543)(0.1927819)

RISK0.4028744**0.2589551***0.2525515***

(0.1690424)(0.0642794)(0.0645149)

MAJOR0.74920090.9759309***0.967264***

(0.5792261)(0.2909073)(0.2913237)

GROUP-0.03581430.4395276*0.4433886*

(0.5980268)(0.2616858)(0.2622353)

BOMA0.1905198-0.5933166**-0.5951297**

(0.680697)(0.2586237)(0.2593034)

DEDUCT0.32504990.31857860.3153274

(0.6133695)(0.2631442)(0.2635156)

Constant-6.972874***-0.3318901-0.2996076

(1.110147)(0.3377757)(0.3390073)

Number of Observations552552552

Log Likelihood-78.03-352.11-351.54

Significance levels p: *** p < 0.01; ** p < 0.05; * p < 0.1.The standard errors are stated in parenthesis below the coefficients.

Table 8: Logistic Regression for fictitious claims

Table 9 displays the results concerning claim build-up. Here, the influencing factors do not have a consistent impact for all models. However, a negative perception of the insurance industry in general only has a positive effect on agreement to fraud in the questionnaire. Somehow surprisingly, participants with real-world claim experience have a higher

acceptance of claim build-up in the questionnaire. But a higher number of insurance

contracts has only in the questionnaire the opposite effect. As before, we find significant effects for , and in the experiment. Participants in treatments with

deductible contracts have a higher tendency to defraud in the experiment and reply in the questionnaire that claim inflation is acceptable. This finding is intuitive, since deductible 23

contracts may be perceived as unfair. As before, participants in larger groups with less transaction costs do defraud more often in the experiment. But in the questionnaire (after the experiment) these participants state a lower acceptance of claim build-up. Participants in bonus-malus treatment state a higher acceptance in the questionnaire with respect to claim build-up. This is very surprising, because premium increases after losses are not affected by any claim inflation activities. Again, the stated attitude towards claim build-up does not significantly affect the behavior in the experiment. The regression results show that for both fraud types the impact of potential influencing factors on insurance fraud differs for fraud in the questionnaire and fraud in the experiment.



QuestionnaireExperimentExperiment

CLAIM BUILD-UP---0.1130159


SATISFACTION-0.1177510.05913650.0565623

(0.2741898)(0.2634079)(0.2634022)

NEGATIVE VIEW0.6352588***0.07625320.0913426

(0.0807376)(0.0687932)(0.0741779)

CLAIMS0.3747613*0.12652390.1358467

(0.209563)(0.2016172)(0.2023874)

CONTRACTS-0.1639345*0.04508520.0415258

(0.0846644)(0.079509)(0.0797409)

FEMALE-0.2854212-0.4239537**-0.4308138**

(0.2094693)(0.1994057)(0.1998664)

RISK0.10340240.1138266*0.1159491*

(0.0676409)(0.0644989)(0.0646079)

MAJOR0.16673170.8038195***0.8064087***

(0.3008636)(0.2926293)(0.2923967)

GROUP-0.840668***0.7294585***0.7094687***

(0.2847806)(0.2672125)(0.2695875)

BOMA0.4953708*0.19115550.2037659

(0.2810854)(0.2665228)(0.2674406)

DEDUCT0.5471097*0.5648161**0.5788966**

(0.2849863)(0.2683106)(0.2698744)

Constant-2.041513***-0.9778661***-0.9703043***

(0.3906996)(0.3579997)(0.3582354)


Log Likelihood-303.29-324.94-324.79


Table 9: Logistic regressions for claim build-up

24

Robustness checks with reduced number of participants for Model 2a and Model 2b are provided in Appendix 6. Again, we excluded participants who agreed with claim build-up in the questionnaire but do not agree with this fraud type in the experiment. We now findconformity betweentheir answers and their behavior for the variablesWe

as well asand. ,

still find only weaksignificant effects for recent claiming experiencein the

,,,,,

of the experiment.

questionnaire. But the impact is insignificant when fraud is generated out()

Participants belonging to the bonus-malus treatment have significantly higher acceptance of claim build-up in the questionnaire. But this effect is still not observable when fraud comes out of the experiment.

5 Conclusion

When analyzing the extent and influencing factors for insurance fraud, consumer surveys are an important data source. Given that insurance fraud is a sensitive topic, an important question is how reliable this kind of data is. We match individuals expressed attitude towards insurance fraud in a questionnaire with their behavior in an experiment. For the fraud types fictitious claims and claim build-up, we only find a weak link between the stated attitude toward fraud in the questionnaire and the behavior in the experiment. For almost half of the participants the self-stated attitude in the questionnaire does not coincide with their behavior in the experiment. Hence, individuals do not accept insurance fraud in the questionnaire, but cheat in the experiment. When closer analyzing factors that might influence the attitude towards insurance fraud and respective behavior, we do not find many common effects of influencing factors.

Of course a lab experiment is far from being realistic. Therefore, the generalizability of our experiment is definitely limited. However, in an experiment, talk is not cheap. Behavior has monetary consequences and participants may not feel a direct pressure to behave or state attitudes according to social standards. Given our findings, we would be cautious to take survey results on the self-stated attitude with respect to insurance fraud at face value. But due to the fact that fraud is a major problem for the insurance industry, it seems worthwhile to further explore.

25

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Appendix 1: Questionnaire Data Fraudulent behavior

Fictitious claims:In general, I can imagine to report a claim that never occurred

to receive an indemnity from the insurance company.

Claims exaggeration:In general, I can imagine to slightly exaggerate claims in case a

loss occurred.

Redefinition of claims:In general, in case a loss occurred, I can imagine to report

wrong information regarding the course of events towards the

insurance company to receive an indemnity at all.

Insurance fraud in general: In general, it is acceptable to report wrong information to the

insurer.

29

Appendix 2: Alternative Segmentation

Fictitious claims







Note: The table includes the observations of 552 individuals. 24 observations are not included because their attitude concerning fictitious claims is categorized as neutral in the questionnaire. The number of observations for each category is stated in parenthesis below the percentages. A McNemars chi-square statistic indicates a statistically significant difference at a 1 percent level.

Table 10: Match of fictitious claims in experiment and questionnaire

Claim build-up







Note: The table includes the observations of 507 individuals. 69 observations are not included because their attitude concerning claim build- up is categorized as neutral in the questionnaire. The number of observations for each category is stated in parenthesis below the percentages. A McNemars chi-square statistic indicates a statistically significant difference at a 1 percent level.

Table 11: Match of claim build-up in experiment and questionnaire

Fraud in general







Note: The table includes the observations of 530 individuals. 46 observations are not included because their attitude concerning fraud in general is categorized as neutral in the questionnaire. The number of observations for each category is stated in parenthesis below the percentages. A McNemars chi-square statistic indicates a statistically significant difference at a 1 percent level.

Table 12: Match of fraud in general in experiment and questionnaire

30

Appendix 3: Questionnaire Data Independent variables

SATISFACTION:

SATISFACTION GENERAL: How satisfied are you in general with your insurance? (Please skip the question if you do not have any insurance contracts)

SATISFACTION CONTRACTS: If you already have claimed a loss, how satisfied have you been with the claims settlement process? (Please skip the question if you did not claim a loss)

NEGATIVE VIEW:

COMPLICATED CONTRACTS: To make a lot of money, insurance companies construct their contracts very complicated.

EARNINGS:Insurance companies earn so much money, that it is no

problem if customers retrieve their paid premiums.

RECENT CLAIMS:When did you lastly claim a loss?

CONTRACTS:How many insurance contracts do you have (not concluded by

your parents)?

FEMALE:What is your gender?

RISK:How do you assess yourself: Are you in general more risk

seeking or do you try to avoid risks?

MAJOR:What are you studying?

31

Appendix 4: Regression table Model 3



FRAUD IN GENERAL--0.5092978


SATISFACTION0.2777229-0.0472918-0.0589142

(0.3447836)(0.2665708)(0.2679711)

NEGATIVE VIEW0.5440002***0.10289640.0769129

(0.1103533)(0.0711045)(0.072878)

CLAIMS0.6133291**-0.075904-0.1056978

(0.2959884)(0.2038155)(0.2051018)

CONTRACTS-0.1902664-0.0736677-0.0653826

(0.1304612)(0.0786408)(0.0790023)

FEMALE-0.4274328-0.2829882-0.2657588

(0.2880471)(0.2040918)(0.2047709)

RISK0.11348350.2487395***0.2456196***

(0.0936771)(0.066885)(0.0670047)

MAJOR-0.67578930.7900403**0.8147962**

(0.4863195)(0.3148028)(0.3155086)

GROUP-0.4879310.9805861***1.011056***

(0.3938714)(0.2880899)(0.2895946)

BOMA0.2774118-0.2950543-0.3070216

(0.3786757)(0.2614914)(0.2625096)

DEDUCT0.32859570.30960840.2924325

(0.3934913)(0.2791766)(0.2800345)

Constant-3.98013***-0.4802794-0.4624968

(0.5850051)(0.3610004)(0.3616341)


Log Likelihood-177.62-324.23-322.87


Table 13: Logistic regressions for fraud in general

32

Appendix 5: Regression table Model 1 with reduced number of participants


Model 1aModel 1b

QuestionnaireExperiment

SATISFACTION-0.1372120.1713012

(0.843709)(0.263439)

NEGATIVE VIEW0.762166***0.0173268

(0.2285536)(0.0693252)

CLAIMS1.083246*-0.2745966

(0.5975732)(0.1976486)

CONTRACTS0.0449196-0.0901808

(0.2328893)(0.0764041)

FEMALE-0.6761494-0.4076423**

(0.5795864)(0.1949906)

RISK0.4688845**0.27348***

(0.2091866)(0.0652968)

MAJOR0.65082211.076541***

(0.7193627)(0.3021016)

GROUP0.39824280.3858759

(0.7533969)(0.2658366)

BOMA-0.1365368-0.5608003**

(0.8381087)(0.2610179)

DEDUCT-0.16942670.412277

(0.7858404)(0.2691268)

Constant-8.028374***-0.3563582

(1.356513)(0.3405476)


Log Likelihood-57.85-344.69


Table 14: Logistic Regression for fictitious claims

33

Appendix 6: Regression table Model 2 with reduced number of participants


Model 2aModel 2b

QuestionnaireExperiment

SATISFACTION-0.109019-0.1707937

(0.3342281)(0.3364634)

NEGATIVE VIEW0.6371252***0.3926565***

(0.0998702)(0.0919389)

CLAIMS0.4700271*0.1788879

(0.2607505)(0.252064)

CONTRACTS-0.0730495-0.000414

(0.1072196)(0.0930073)

FEMALE-0.6248819**-0.5704957**

(0.2590952)(0.2525971)

RISK0.1623418*0.1474814*

(0.0838444)(0.0809779)

MAJOR0.6287427*0.7741426**

(0.341885)(0.3669833)

GROUP-0.41467230.2128278

(0.3349029)(0.3475813)

BOMA0.686782*0.4362015

(0.3589225)(0.3377403)

DEDUCT0.7971227**0.9377498**

(0.3553193)(0.3630112)

Constant-3.268936***-1.075555**

(0.5014541)(0.435562)


Log Likelihood-196.65-214.70


Table 15: Logistic Regression for claim build-up

34

Review

Documents

introduction insurance

insurance sector

insurance companies

major forms of insurance

insurance research council

automobile insurance

experiment participants

costly activities