Outlier analysis for plastic card fraud detection a hybridized and multi-objective approach

Nowadays, plastic card fraud detection is of great importance to financial institutions. This paper presents a proposal for an automated credit card fraud detection system based on the outlier analysis technology. Previous research has established that the use of outlier analysis is one of the best techniques for the detection of fraud in general. However, to establish patterns to identify anomalies, these patterns are learned by the fraudsters and then they change the way to make de fraud. The approach applies a multi-objective model hybridized with particle swarm optimization of typical cardholder's behavior and to analyze the deviation of transactions, thus finding suspicious transactions in a non supervised scheme.