Assessing Consumer Fraud Risk in Insurance Claims

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 highvolume 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.     

A multi-dimensional analysis of the subjective well-being of self-initiated expatriates: The case of Nigerian expatriates in Germany

This paper examines the role of four types of influences on the Subjective Well-Being of Nigerian self-initiated expatriates in Germany: (1) Individual, (2) Group, (3) Social, and (4) Organizational. Based on survey data from 377 respondents, we find that variables in all four categories influence subjective well-being. Our findings generally agree with the results predicted by the theory, albeit a few counter-intuitive findings. Above all, our results indicate the potency of social- and group-level influences. The individual- and organizational-level influences, too, are significant, although to a slightly lesser extent. Overall, our results suggest that “soft” factors play an important role in augmenting perceptions about one's own well-being in the context of international assignments.     

Cost management and corporate payout decisions

Review of Quantitative Finance and Accounting - We investigate the relationship between cost management and dividend payout decisions. Prior studies document that firms with consistent earnings or...