Fraud Detection Using a Multinomial Logit Model With Missing Information
Steven B Caudill (),
Mercedes Ayuso and
Montserrat Guillen ()
Journal of Risk & Insurance, 2005, vol. 72, issue 4, 539-550
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.
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9) Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:bla:jrinsu:v:72:y:2005:i:4:p:539-550
Ordering information: This journal article can be ordered from
Access Statistics for this article
Journal of Risk & Insurance is currently edited by Keith Crocker
More articles in Journal of Risk & Insurance from The American Risk and Insurance Association Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().