Multiple algorithms for fraud detection

摘要

This paper describes an application of Case-Based Reasoning to the problem of reducing the number of final-line fraud investigations in the credit approval process. The performance of a suite of algorithms, which are applied in combination to determine a diagnosis from a set of retrieved cases, is reported. An adaptive diagnosis algorithm combining several neighbourhood-based and probabilistic algorithms was found to have the best performance, and these results indicate that an adaptive solution can provide fraud filtering and case ordering functions for reducing the number of final-line fraud investigations necessary.