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A1616
Title: Fraud detection by a multinomial model: Separating honesty from unobserved fraud Authors:  Jonas Andersson - Norwegian School of Economics (Norway) [presenting]
Andreas Olden - Norwegian School of Economics (Norway)
Aija Rusina - Norwegian School of Economics (Norway)
Abstract: With the problem of detecting tax evasion in mind, we investigate how to identify items, e.g. individuals or companies, that are wrongly classified as honest. Normally, we observe two groups of items, labeled fraudulent and honest, but suspect that many of the observationally honest items are, in fact, fraudulent. The items observed as honest are therefore divided into two unobserved groups, honestH, representing the truly honest, and honestF, representing the items that are observed as honest, but that are actually fraudulent. By using a multinomial logit model and assuming commonality between the observed fraudulent and the unobserved honestF, a method that uses the EM-algorithm to separate them has been previously presented. By means of a Monte Carlo study, we investigate how well the method performs, and under what circumstances. We then compare it to other standard methods.