Optimizing credit card fraud detection by combining behavioral profiling, clustering, and LSTM

Credit card fraud poses a substantial risk to both financial institutions and consumers, necessitating sophisticated detection methods. This paper introduces an innovative approach to enhancing credit card fraud detection by integrating behavioral profiling, clustering, and Long Short-Term Memory (LSTM) neural networks. This methodology exploits the combined strengths of these techniques to improve the precision and efficiency of fraud detection systems. Behavioral profiling identifies the unique spending habits and characteristics of individuals, while clustering organizes similar accounts based on these profiles. LSTM models are then utilized to learn the temporal dependencies and sequential patterns within each cluster, enabling precise detection of fraudulent transactions. This integration effectively addresses the challenges of class imbalance and the complexity of fraud patterns in the data. The proposed approach is tested on a real-world credit card transaction dataset, showcasing its superior performance in terms of Accuracy, F1-score and AUC-ROC compared to conventional methods.