Creating a credit card anti-fraud detection sytem using machine learning

Abstract

With the increasing prevalence of online transactions, fraudulent cases involving credit cards have also been on the rise. Therefore, the primary objective of this research is to create an effective fraud detection system that benefits both financial companies and their cardholders. The research work began with a thorough analysis of the dataset, which helped to provide a better understanding of the data. In order to enhance the performance of the machine learning models, new features were created by combining previous transaction features to identify clients and credit cards. To mitigate the problem of imbalanced data, a minority oversampling method was utilized. Machine learning techniques such as XGboost and Random Forest were then employed to evaluate the model performances based on AUC, recall and F1-score. The results demonstrated that the models improved significantly after incorporating the combined features to identify clients and users.