Feature Selection Approaches to Fraud Detection in e-Payment Systems

摘要

Due to the large amount of data generated in electronic transactions, to find the best set of features is an essential task to identify frauds. Fraud detection is a specific application of anomaly detection, characterized by a large imbalance between the classes, which can be a detrimental factor for feature selection techniques. In this work we evaluate the behavior and impact of feature selection techniques to detect fraud in a Web Transaction scenario. To measure the effectiveness of the feature selection approach we use some state-of-the-art classification techniques to identify frauds, using real application data. Our results show that the imbalance between the classes reduces the effectiveness of feature selection and that resampling strategy applied in this task improves the final results. We achieve a very good performance, reducing the number of features and presenting financial gains of up to 57.5% compared to the actual scenario of the company.