Cost-sensitive Heterogeneous Integration for Credit Card Fraud Detection

Credit card fraudulent activities cause huge financial losses around the world every year. In recent years, data-driven methods are increasingly becoming fraud detection methods for financial institutions. Some related studies based on machine learning and data mining have been proposed. However, most of them do not consider the actual financial losses associated with the fraud detection process. Or some related cost sensitive methods focus on minimizing cost loss but the accuracy of detection is low. This paper presents a cost-sensitive heterogeneous integration model, CSHIM, for credit card fraud detection. CSHIM considers the different misclassification costs of each transaction and integrates the superior performance of different individual classifiers through the cost-sensitive weighted Dempster-Shafer fusion theory to achieve better fraud detection results. The goal is to achieve good performance not only in reducing monetary losses but also improving detection accuracy. We have done experiments on public data sets, the experiments show that the method proposed in this paper can save up to 74.69% of the cost, which not only can achieve good results in cost savings, but also has better performance of other standard metrics compared with other methods.