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Enhancing Bank Credit Card Transaction Fraud Detection with Machine Learning Techniques
- 1 University of Maryland
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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.
Keywords
Credit Card Fraud, Machine Learning, Random Forest, Neural Network, XGBoost
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Cite this article
Zhu,Y. (2024). Enhancing Bank Credit Card Transaction Fraud Detection with Machine Learning Techniques. Advances in Economics, Management and Political Sciences,122,71-81.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 8th International Conference on Economic Management and Green Development
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