Fraud Detection Using a Multinomial Logit Model with Missing Information

12 Pages Posted: 30 Dec 2005

See all articles by Steven B. Caudill

Steven B. Caudill

Auburn University - Department of Economics

Mercedes Ayuso

University of Barcelona

Montserrat Guillen


Recently, Artis, Ayuso, and Guillen (2002, Journal of Risk and Insurance 69: 325-340; henceforth AAG) estimate a logit model using claims data. Some of the claims are categorized as 'honest' and other claims are known to be fraudulent. Using the approach of Hausman, Abrevaya, and Scott-Morton (1998 Journal of Econometrics 87: 239-269), AAG estimate a modified logit model allowing for the possibility that some claims classified as 'honest' might actually be fraudulent. Applying this model to data on Spanish automobile insurance claims, AGG find that 5 percent of the fraudulent claims go undetected. The purpose of this article is to estimate the model of AAG using a logit model with missing information. A constrained version of this model is used to reexamine the Spanish insurance claim data. The results indicate how to identify misclassified claims. We also show how misclassified claims can be identified using the AAG approach. We show that both approaches can be used to probabilistically identify misclassified claims.

Suggested Citation

Caudill, Steven B. and Ayuso, Mercedes and Guillen, Montserrat, Fraud Detection Using a Multinomial Logit Model with Missing Information. Journal of Risk and Insurance, Vol. 72, No. 4, pp. 539-550, December 2005, Available at SSRN: or

Steven B. Caudill (Contact Author)

Auburn University - Department of Economics ( email )

415 W. Magnolia
Auburn, AL 36849-5242
United States
334-844-2907 (Phone)

Mercedes Ayuso

University of Barcelona ( email )

Av. Diagonal 690
Barcelona, E-08034
+34 934 021409 (Phone)
+34 934 021 821 (Fax)

No contact information is available for Montserrat Guillen

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