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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
HAL Paris Nanterre
Article . 2009
Data sources: HAL Paris Nanterre
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
SSRN Electronic Journal
Article . 2003 . Peer-reviewed
Data sources: Crossref
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Optimal Auditing for Insurance Fraud

Authors: Dionne, Georges; Giuliano, Florence; Picard, Pierre;

Optimal Auditing for Insurance Fraud

Abstract

This article aims at making a bridge between the theory of optimal auditing and current procedures applied to audit files in different markets where scoring is the instrument used to implement an audit strategy. The literature has not yet developed an optimal audit policy for the scoring methodology. Our application is meant for the audit of insurance fraud but can be applied to many other activities that use the scoring approach. On the theoretical side, we show that the optimal auditing strategy takes the form of a "Red Flags Strategy" which consists in referring claims to a Special Investigation Unit (SIU) when certain fraud indicators are observed. Fraud indicators are classified based on the degree to which they reveal an increasing probability of fraud and this strategy remains optimal if the investigation policy is budget constrained. Moreover, the auditing policy acts as a deterrence device. On the empirical side, four significant results are obtained with data from a large European insurance company. First, we are able to compute a critical suspicion index for fraud, providing a threshold above which all claims must be audited. Secondly, we obtain that if the insurer applies this policy, he will save more than E22 million, which represents 43% of the current cost of fraudulent claims. Thirdly, we show that it is possible to improve these results by using information capable of isolating different groups of insureds with different morale costs of fraud. Finally, our results indicate how the deterrence effect of the audit scheme can be taken into account and how it affects the optimal auditing strategy.

Country
France
Keywords

scoring, suspicion index, insurance fraud, audit, [SHS.ECO]Humanities and Social Sciences/Economics and Finance, moral cost of fraud, fraud indicators, red flags strategy, signal manipulation, deterrence effect, [SHS.GESTION]Humanities and Social Sciences/Business administration, Optimal audit, scoring methodology, insurance fraud, red flags strategy, fraud indicators, suspicion index, morale cost of fraud, [SHS.GESTION] Humanities and Social Sciences/Business administration, [SHS.ECO] Humanities and Social Sciences/Economics and Finance, jel: jel:D81, jel: jel:G22, jel: jel:G14

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
11
Average
Top 10%
Average
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