<scp>Fraud Detection Using a Multinomial Logit Model With Missing Information</scp>

TLDR

AAG applied a modified logit model to Spanish automobile insurance claims that permits honest claims to be misclassified as fraudulent. This study aims to estimate AAG’s modified logit model incorporating missing information. The authors employ a constrained version of the AAG model to reanalyze the Spanish claim dataset. The constrained model reveals that about 5 % of fraudulent claims remain undetected and offers a probabilistic method to identify misclassified claims.

Abstract

Abstract Recently, Artís, Ayuso, and Guillén (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.