Fraud Detection in Online Banking Using Machine Learning

  • Authors

    • Noah Wright Data Engineering Lead, SAP, Germany. Author
    • Isabella Moore HR Director, Siemens, Germany. Author

    DOI:

    https://doi.org/10.67228/3071642X/IJCFDE-2021PI3C9D

    Published 04-02-2021

  • Online Banking, Fraud Detection, Machine Learning, Financial Security, Random Forest, Transaction Analysis, Cybersecurity, Data Mining

    Issue

    Vol. 4 No. 1 (2021)

    Section

    Articles

    How to Cite

    Wright, N., & Moore, I. (2021). Fraud Detection in Online Banking Using Machine Learning. International Journal of Commerce, Finance and Digital Economy, 4(1), 01-17. https://doi.org/10.67228/3071642X/IJCFDE-2021PI3C9D

  • Abstract

    Machine Learning-based fraud detection systems provide an effective and intelligent solution for securing online banking platforms against evolving cyber threats. By analyzing transaction patterns and detecting anomalies in real time, ML algorithms such as Logistic Regression, Decision Tree, Random Forest, and XGBoost can accurately identify fraudulent activities while reducing false alarms. Experimental results indicate that ensemble models, particularly Random Forest, achieve superior detection performance. The proposed framework enhances fraud prevention, minimizes financial losses, improves customer trust, and offers a scalable and adaptive approach for modern digital banking security.

  • References

    [1] Dal Pozzolo, A., Boracchi, G., Caelen, O., Alippi, C., & Bontempi, G. (2015). Credit card fraud detection: A realistic modeling and a novel learning strategy. IEEE Transactions on Neural Networks and Learning Systems, 29(8), 3784–3797.

    [2] Bhattacharyya, S., Jha, S., Tharakunnel, K., & Westland, J. C. (2011). Data mining for credit card fraud: A comparative study. Decision Support Systems, 50(3), 602–613.

    [3] Phua, C., Lee, V., Smith, K., & Gayler, R. (2010). A comprehensive survey of data mining-based fraud detection research. arXiv preprint arXiv:1009.6119.

    [4] West, J., & Bhattacharya, M. (2016). Intelligent financial fraud detection: A comprehensive review. Computers & Security, 57, 47–66.

    [5] Ngai, E. W. T., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50(3), 559–569.

    [6] Carcillo, F., Le Borgne, Y. A., Caelen, O., Bontempi, G., & Mazzer, Y. (2019). Combining unsupervised and supervised learning in credit card fraud detection. Information Sciences, 557, 317–331.

    [7] Fiore, U., De Santis, A., Perla, F., Zanetti, P., & Palmieri, F. (2019). Using generative adversarial networks for improving classification effectiveness in credit card fraud detection. Information Sciences, 479, 448–455.

    [8] Jurgovsky, J., Granitzer, M., Ziegler, K., Calabretto, S., Portier, P. E., He-Guelton, L., & Caelen, O. (2018). Sequence classification for credit-card fraud detection. Expert Systems with Applications, 100, 234–245.

    [9] Roy, A., Sun, J., Mahoney, R., Alonzi, L., Adams, S., & Beling, P. A. (2018). Deep learning detecting fraud in credit card transactions. Systems and Information Engineering Design Symposium (SIEDS), IEEE, 129–134.

    [10] Sahin, Y., & Duman, E. (2011). Detecting credit card fraud by decision trees and support vector machines. Proceedings of the International MultiConference of Engineers and Computer Scientists, 1, 442–447.

    [11] Bahnsen, A. C., Aouada, D., Ottersten, B., & Stojanovic, A. (2016). Feature engineering strategies for credit card fraud detection. Expert Systems with Applications, 51, 134–142.

    [12] Whitrow, C., Hand, D. J., Juszczak, P., Weston, D., & Adams, N. M. (2009). Transaction aggregation as a strategy for credit card fraud detection. Data Mining and Knowledge Discovery, 18(1), 30–55.

    [13] Le Borgne, Y. A., & Bontempi, G. (2005). Machine learning for credit card fraud detection: Practical issues and challenges. International Conference on Computational Intelligence for Financial Engineering, IEEE, 62–68.

    [14] Randhawa, K., Loo, C. K., Seera, M., Lim, C. P., Nandi, A. K., & Lal, A. N. (2018). Credit card fraud detection using AdaBoost and majority voting. IEEE Access, 6, 14277–14284.

    [15] Abdallah, A., Maarof, M. A., & Zainal, A. (2016). Fraud detection system: A survey. Journal of Network and Computer Applications, 68, 90–113.

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