Assessing Consumer Fraud Risk in Insurance Claims: An Unsupervised Learning Technique Using Discrete and Continuous Predictor Variables

North American Actuarial Journal, Vol. 13, No. 4, pp. 438-458, 2009

41 Pages Posted: 15 May 2011 Last revised: 20 Aug 2012

See all articles by Jing Ai

Jing Ai

University of Hawaii at Manoa - Shidler College of Business

Patrick L. Brockett

University of Texas at Austin - Department of Information, Risk and Operations Management

Linda L. Golden

University of Texas at Austin - Red McCombs School of Business

Date Written: December 1, 2009

Abstract

We present an unsupervised learning method for classifying consumer insurance claims according to their suspiciousness of fraud versus nonfraud. The predictor variables contained within a claim file that are used in this analysis can be binary, ordinal categorical, or continuous variates. They are constructed such that the ordinal position of the response to the predictor variable bears a monotonic relationship with the fraud suspicion of the claim. Thus, although no individual variable is of itself assumed to be determinative of fraud, each of the individual variables gives a "hint" or indication as to the suspiciousness of fraud for the overall claim file. The presented method statistically concatenates the totality of these "hints" to make an overall assessment of the ranking of fraud risk for the claim files without using any a priori fraud-classified or labeled subset of data.

We first present a scoring method for the predictor variables that puts all the variables (whether binary "red flag indicators," ordinal categorical variables with different categories of possible response values, or continuous variables) onto a common 1 to 1 scale for comparison and further use. This allows us to aggregate variables with disparate numbers of potential values. We next show how to concatenate the individual variables and obtain a measure of variable worth for fraud detection, and then how to obtain an overall holistic claim file suspicion value capable of being used to rank the claim files for determining which claims to pay and the order in which to investigate claims further for fraud. The proposed method provides three useful outputs not usually available with other unsupervised methods: (1) an ordinal measure of overall claim file fraud suspicion level, (2) a measure of the importance of each individual predictor variable in determining the overall suspicion levels of claims, and (3) a classification function capable of being applied to existing claims as well as new incoming claims. The overall claim file score is also available to be correlated with exogenous variables such as claimant demographics or high volume physician or lawyer involvement. We illustrate that the incorporation of continuous variables in their continuous form helps classification and that the method has internal and external validity via empirical analysis of real data sets. A detailed application to automobile bodily injury fraud detection is presented.

Keywords: Unsupervised classification, Latent variable model, RIDIT analysis, Insurance fraud detection, Missing Dependent Variable

Suggested Citation

Ai, Jing and Brockett, Patrick L. and Golden, Linda L., Assessing Consumer Fraud Risk in Insurance Claims: An Unsupervised Learning Technique Using Discrete and Continuous Predictor Variables (December 1, 2009). North American Actuarial Journal, Vol. 13, No. 4, pp. 438-458, 2009. Available at SSRN: https://ssrn.com/abstract=1841804

Jing Ai (Contact Author)

University of Hawaii at Manoa - Shidler College of Business ( email )

2404 Maile Way
Honolulu, HI 96822
United States

Patrick L. Brockett

University of Texas at Austin - Department of Information, Risk and Operations Management ( email )

CBA 5.202
Austin, TX 78712
United States

Linda L. Golden

University of Texas at Austin - Red McCombs School of Business ( email )

Austin, TX 78712
United States

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