Does insurance fraud in automobile theft insurance fluctuate with the business cycle?

[[abstract]]Financial institutions face various cyclical risks, but very few studies have analyzed the cyclicality of operational risk. External fraud is an important operational risk faced by insurers. In this research, we analyze the empirical relationship between insurance fraud and business cycle and we concentrate our study on two insurance contracts that may create an incentive to defraud. We find that residual insurance fraud exists both in the contract with replacement cost endorsement and the contract with no-deductible endorsement in the Taiwan automobile theft insurance market. These results are consistent with previous literature on the relationship between fraud activity and non-optimal insurance contracting. We also show that the severity of insurance fraud is countercyclical. Fraud is stimulated during periods of recession and mitigated during periods of expansion. Although this last result seems intuitive, our contribution is the first to measure its significance.[[notice]]補正完畢[[incitationindex]]SSCI[[booktype]]紙本[[booktype]]電子

Auto claim fraud detection using Bayesian learning neural networks

This article explores the explicative capabilities of neural network classifiers with automatic relevance determination weight regularization, and reports the findings from applying these networks for personal injury protection automobile insurance claim fraud detection. The automatic relevance determination objective function scheme provides us with a way to determine which inputs are most informative to the trained neural network model. An implementation of MacKay's, (1992a,b) evidence framework approach to Bayesian learning is proposed as a practical way of training such networks. The empirical evaluation is based on a data set of closed claims from accidents that occurred in Massachusetts, USA during 1993.status: publishe