Automatic credit card fraud detection based on non-linear signal processing

Fraud detection is a critical problem affecting large financial companies that has increased due to the growth in credit card transactions. This paper presents a new method for automatic detection of frauds in credit card transactions based on non-linear signal processing. The proposed method consists of the following stages: feature extraction, training and classification, decision fusion, and result presentation. Discriminant-based classifiers and an advanced non-Gaussian mixture classification method are employed to distinguish between legitimate and fraudulent transactions. The posterior probabilities produced by classifiers are fused by means of order statistical digital filters. Results from data mining of a large database of real transactions are presented. The feasibility of the proposed method is demonstrated for several datasets using parameters derived from receiver characteristic operating analysis and key performance indicators of the business.