Enhancing Bank Credit Card Transaction Fraud Detection with Machine Learning Techniques

Abstract: Because of the increasing prevalence and sophistication of credit card theft, standard detection measures frequently fail. This study investigates the use of several machine learning algorithms to improve fraud detection in bank credit card transactions. The research aims to develop enhanced fraud detection systems by employing an integrated strategy involving data preprocessing, the application of various classification algorithms, and performance evaluation. The study thoroughly examines algorithms such as Random Forest, Logistic Regression, and Neural Networks, focusing on their predictive capabilities and practical applications. The findings indicate that machine learning provides a robust framework for increasing the accuracy and efficiency of fraud detection systems, consequently assisting financial institutions in protecting customer transactions and strengthening security measures. Moreover, by prioritizing robust model selection and feature engineering, banks can significantly enhance their fraud detection performance. This research could greatly influence how financial institutions handle fraud, potentially reducing losses, improving operational efficiency, and securing transaction environments. The insights gained from this study are also valuable for informing broader financial security strategies and guiding future research in the field.