A Machine Learning Approach for Credit Card Fraud Detection using Behavioral and Spatial Features

Abstract

For identifying fraud within a vast array of digitally conducted financial transactions, it is imperative for financial transaction systems to be scalable and accurate. One of the common uses of machine learning in the financial sector is credit card fraud detection, particularly when manual monitoring of the massive volume of financial transactions is impractical. The proposed system is designed using the XGBoost classifier. This approach improves the ability to detect fraudulent credit card transactions through various behavioral and geographical aspects, including transaction time, transaction frequency, and the distance between the cardholder and merchant locations. Experimental results indicate that the proposed system is capable of detecting fraudulent transactions with a precision of approximately 99.8% and a high ROC-AUC score. The scalable framework designed in this study can assist financial organizations in improving the security and effectiveness of fraud detection systems.