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Schmalenbach Business Review
Erschienen in: Schmalenbach Business Review 2/2018

03.11.2017 | Original Article

Can we Trust Consumers’ Survey Answers when Dealing with Insurance Fraud?

Evidence from an Experiment

verfasst von: Kerstin Fiederling, Jörg Schiller, Frauke von Bieberstein

Erschienen in: Schmalenbach Business Review | Ausgabe 2/2018

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Abstract

Consumer surveys (e. g., questionnaires, telephone surveys) are important means to measure the acceptability and willingness to commit insurance fraud as well as related influencing factors. However, for such a sensitive issue, it is unclear to what extent individuals’ stated attitudes correspond to actual behavior. We use a two-stage within-subject procedure that consists of an experiment and a questionnaire. In the experiment, participants are incentivized and have the opportunity to commit fraud by claiming losses that have not occurred or by exaggerating occurred losses. When comparing participants’ behavior in the lab experiment with their answers to a standard survey, we do not find a strong correlation between self-stated attitudes toward insurance fraud and behavior in the experiment.
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Fußnoten
1
See e. g. Weber et al. (2014) for a survey on studies on tax evasion.
 
2
There is no predominant major. The share of participants with a major in business or economics is 14%.
 
3
Auditing activities and fines could trigger strategic considerations and relate to risk aversion. For example, Nagin and Pogarsky (2003) experimentally evaluate the impact of auditing and fines on the extent of dishonest behavior and Grolleau et al. (2016) study the impact of monitoring activities on the self-reported performance of experimental participants in a real effort task.
 
4
For more details, please see von Bieberstein and Schiller (2017) and Lammers and Schiller (2010).
 
5
Note that from an ex-ante perspective, honest risk-averse individuals without prosocial preferences should claim indemnities that match their losses in order to have complete coverage at low transaction costs.
 
6
In the BoMa Treatment, monetary gains from fraud can only be derived via backwards induction. The net benefit of an indemnity payment in each period is still \(I-\frac{1}{n} (1+k )I\). However, if a positive claim is made, the premium in each future period will be increased by 2 points. Otherwise, the premium in each future period will be decreased by 1 point. As the net gains are always positive, rational and purely selfish individuals should also in the BoMa Treatment always claim the maximum possible indemnities. See von Bieberstein and Schiller (2017) for further details.
 
7
For instance, in Fischbacher and Föllmi-Heusi (2013), 20% of the subjects only commit partial lying by not telling the truth but not lying to the full extent either. This behavior is consistent with convex lying costs. Note that the model could easily be extended to psychological costs \(\theta _{i} (K+ (I (x_{j} )-x_{j} )^{2} )\) for \(I (x_{j} )>x_{j}\). This would incorporate both, a fixed psychological cost \(K\) for each time fraud is committed as well as a factor \(I (x_{j} )-x_{j}\) depending on the size of the amount defrauded.
 
8
For instance, consider the case of small groups \(n=4\) and high transaction costs \(k=0.4\) where preferences according to (1) are given as \(U_{i}=W+ (0.65-1.05\nu _{i} )I-x_{j}-\kappa _{i}\). Assume \(\nu _{i}=0.5\), i. e., the individual only values half of the other group members’ monetary cost. When the individual decides which indemnity to claim in case of no loss \(x_{0}\), individuals with \(\theta _{i}>0.0125\) will honestly claim \(I_{0}\), individuals with \(\theta _{i}\in [0.005,0.0125 ]\) will commit some fraud by claiming \(I_{L}=10\), and individuals with \(\theta _{i}<0.005\) will commit maximum fraud and claim \(I_{H}=15\).
 
9
Furthermore, the questionnaire contains questions concerning the participants’ attitude toward redefinition of claims and fraud in general. Regarding these two fraud types, no corresponding actions in the experimental part exist. Because we cannot measure redefinition and fraud in general in the experimental part, we will not focus on this type of fraud in the following. The distribution of the participants’ answers for these two fraud types can be found in Table A.1 in the Appendix.
 
10
A comparison of the distributions of all fraud types 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 view 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 a more acceptable form of insurance fraud because an actual loss occurred (Nell 1998; Nell and Schiller 2002).
 
11
For fraud in general, for 43.59% of the participants, the behavior in the experiment corresponds with their stated attitude in the questionnaire. The full table analyzing the match between the answers in the questionnaire and the behavior in the experiment for fraud in general is provided in part B of the Appendix.
 
12
The full matching Table C.1 and C.2 for fictitious claims and claim build-up can be found in part C of the Appendix.
 
13
We are grateful to an anonymous referee for this interpretation.
 
14
In the experiment, fraud in general is specified as either reporting fictitious claims or exaggerating claims or both types of fraud in the respective round of the experiment. In the questionnaire, the attitude toward fraud in general was explicitly asked.
 
15
The analysis was also done for the fraud type “fraud in general”. The results for this fraud type is presented in part D of the Appendix.
 
16
For fraud in the questionnaire: Participants who disagree with the respective fraud type in the questionnaire (answers from 0–2 on the Likert-scale) are considered as “no fraud”. Answers from 4–6 are treated as “fraud”. All participants who take a neutral position were not included in the analysis. For fraud in the experiment: Participants who cheat in 0–2 rounds of the experiment are assigned to “no fraud”. The behavior of participants who cheat in more than 2 rounds is considered as “fraud”.
 
17
The answers to questions used to create the variable SATISFACTION are given on a seven score Likert-scale. In line with the categorization of the previous chapters, we assign answers from 0 to 2 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.
 
18
The answers for the underlying questions of the principal component factor analysis are also given on a seven score Likert-scale.
 
19
See the discussion in von Bieberstein and Schiller (2017).
 
20
Collinearity is tested using variance inflation factors (vif). The factors exhibit the following vif’s: \(\mathrm{vif}_{\mathrm{HONEST}}=1,03,\mathrm{vif}_{\mathrm{VIEW}}=1,03\) and \(\mathrm{vif}_{\mathrm{UNDERSTAND}}=1,02\).
 
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Metadaten
Titel
Can we Trust Consumers’ Survey Answers when Dealing with Insurance Fraud?
Evidence from an Experiment
verfasst von
Kerstin Fiederling
Jörg Schiller
Frauke von Bieberstein
Publikationsdatum
03.11.2017
Verlag
Springer International Publishing
Erschienen in
Schmalenbach Business Review / Ausgabe 2/2018
Print ISSN: 1439-2917
Elektronische ISSN: 2194-072X
DOI
https://doi.org/10.1007/s41464-017-0041-z