Enhancing Mobile Money Security: A Multi-Layered Fraud Detection System Using Machine Learning and Multi-Factor Authentication — Oak Academic Publishing
Research ArticleOpen AccessGoogle Scholar indexed
Enhancing Mobile Money Security: A Multi-Layered Fraud Detection System Using Machine Learning and Multi-Factor Authentication
Software College, Nankai University, Tianjin, China
1 Software College, Nankai University, Tianjin, China
Mobile money services have revolutionized financial inclusion in developing economies, yet they face significant security challenges from increasingly sophisticated fraud attacks. This paper presents a comprehensive multi-layered fraud detection system that integrates Multi-Factor Authentication (MFA) with advanced machine learning algorithms to enhance mobile money transaction security. The proposed system employs a three-layer architecture comprising preventive measures, real-time fraud detection, and intelligent decision-making components. We evaluated three machine learning models—Logistic Regression, Random Forest, and Gradient Boosting—using the PaySim dataset containing 908,213 transactions with 8213 fraud cases. The Random Forest classifier demonstrated superior performance with 99.95%accuracy, 96.77% precision, 93.18% recall, and an F1-score of 94.94%. The system architecture incorporates a five-layer design featuring MFA, ML-based fraud detection, and an automated decision engine. Functional testing across six critical modules validated the system’s reliability and effectiveness. Our results demonstrate that combining preventive authentication mechanisms with intelligent fraud detection significantly reduces false positives while maintaining high fraud detection rates, making it suitable for real-world deployment in mobile money platforms.
Munyendo, L. and Namirembe, P. (2019) Security and Privacy in Mobile Money: A Systematic Literature Review. International Journal of Computer Applications , 182, 1-8.
Liébana-Cabanillas, F., Marinkovic, V., Ramos de Luna, I. and Kalinic, Z. (2018) Predicting the Determinants of Mobile Payment Acceptance: A Hybrid Sem-Neural Network Approach. Technological Forecasting and Social Change , 129, 117-130. https://doi.org/10.1016/j.techfore.2017.12.015
Zavolokina, L., Dolata, M. and Schwabe, G. (2021) The FinTech Phenomenon: Antecedents of Financial Innovation Perceived by the Popular Press. Financial Innovation , 2, 1-16.
Abdallah, A., Maarof, M.A. and Zainal, A. (2016) Fraud Detection System: A Survey. Journal of Network and Computer Applications , 68, 90-113. https://doi.org/10.1016/j.jnca.2016.04.007
Cartella, F., Anunciacão, O., Funabiki, Y., Yamaguchi, D., Akishita, T. and Elshocht, O. (2021) Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data. arXiv: 2101.08030.
Reese, K., Smith, T., Dutson, J., Armknecht, J., Cameron, J. and Seamons, K. (2021) A Usability Study of Five Two-Factor Authentication Methods. Symposium on Usable Privacy and Security (SOUPS), 8-10 August 2021, 357-370.
Carneiro, N., Figueira, G. and Costa, M. (2017) A Data Mining Based System for Credit-Card Fraud Detection in E-Tail. Decision Support Systems , 95, 91-101. https://doi.org/10.1016/j.dss.2017.01.002
Žliobaitė, I., Pechenizkiy, M. and Gama, J. (2016) An Overview of Concept Drift Applications. In: Japkowicz, N. and Stefanowski, J., Eds., Big Data Analysis : New Algorithms for a New Society , Springer, 91-114. https://doi.org/10.1007/978-3-319-26989-4_4
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., et al . (2020) Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI. Information Fusion , 58, 82-115. https://doi.org/10.1016/j.inffus.2019.12.012
Stylios, I., Thanou, O., Androulidakis, I. and Zaitseva, E. (2016) A Review of Continuous Authentication Using Behavioral Biometrics. Proceedings of the South East European Design Automation , Computer Engineering , Computer Networks and Social Media Conference ( SEEDA - CECNSM ), 2016.
Varmedja, D., Karanovic, M., Sladojevic, S., Arsenovic, M. and Anderla, A. (2019) Credit Card Fraud Detection—Machine Learning Methods. 2019 18 th International Symposium INFOTEH - JAHORINA ( INFOTEH ), East Sarajevo, 20-22 March 2019, 1-5. https://doi.org/10.1109/infoteh.2019.8717766
Chen, T. and Guestrin, C. (2016) XGBoost: A Scalable Tree Boosting System. Proceedings of the 22 nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , San Francisco, 13-17 August 2016, 785-794. https://doi.org/10.1145/2939672.2939785
Dong, X., Yu, Z., Cao, W., Shi, Y. and Ma, Q. (2019) A Survey on Ensemble Learning. Frontiers of Computer Science , 14, 241-258. https://doi.org/10.1007/s11704-019-8208-z
Fiore, U., De Santis, A., Perla, F., Zanetti, P. and Palmieri, F. (2019) Using Generative Adversarial Networks for Improving Classification Effectiveness in Credit Card Fraud Detection. Information Sciences , 479, 448-455. https://doi.org/10.1016/j.ins.2017.12.030
Jurgovsky, J., Granitzer, M., Ziegler, K., Calabretto, S., Portier, P., He-Guelton, L., et al . (2018) Sequence Classification for Credit-Card Fraud Detection. Expert Systems with Applications , 100, 234-245. https://doi.org/10.1016/j.eswa.2018.01.037
Rtayli, N. and Enneya, N. (2020) Enhanced Credit Card Fraud Detection Based on Svm-Recursive Feature Elimination and Hyper-Parameters Optimization. Journal of Information Security and Applications , 55, Article ID: 102596. https://doi.org/10.1016/j.jisa.2020.102596
Lopez-Rojas, E.A., Elmir, A. and Axelsson, S. (2016) Paysim: A Financial Mobile Money Simulator for Fraud Detection. The 28 th European Modeling and Simulation Symposium , Larnaca, 26-28 September 2016, 249-255.
Pumsirirat, A. and Yan, L. (2018) Credit Card Fraud Detection Using Deep Learning Based on Auto-Encoder and Restricted Boltzmann Machine. International Journal of Advanced Computer Science and Applications , 9, 18-25. https://doi.org/10.14569/ijacsa.2018.090103
Carcillo, F., Le Borgne, Y., Caelen, O., Kessaci, Y., Oblé, F. and Bontempi, G. (2021) Combining Unsupervised and Supervised Learning in Credit Card Fraud Detection. Information Sciences , 557, 317-331. https://doi.org/10.1016/j.ins.2019.05.042
Pozzolo, A.D., Caelen, O., Johnson, R.A. and Bontempi, G. (2015) Calibrating Probability with Undersampling for Unbalanced Classification. 2015 IEEE Symposium Series on Computational Intelligence , Cape Town, 7-10 December 2015, 159-166. https://doi.org/10.1109/ssci.2015.33
Wedge, R., Kanter, J.M., Veeramachaneni, K., Rubio, S.M., Perez, S.I. (2019) Solving the False Positives Problem in Fraud Prediction Using Automated Feature Engineering. In: Brefeld, U., et al ., Eds., Machine Learning and Knowledge Discovery in Databases , Springer, 372-388.
Yang, W., Zhang, Y., Ye, K., Li, L. and Xu, C. (2019) FFD: A Federated Learning Based Method for Credit Card Fraud Detection. In: Chen, K., Seshadri, S. and Zhang, L.J., Eds., Big Data — BigData 2019, Springer, 18-32. https://doi.org/10.1007/978-3-030-23551-2_2
Liu, Y., Ao, X., Qin, Z., Chi, J., Feng, J., Yang, H., et al . (2021) Pick and Choose: A GNN-Based Imbalanced Learning Approach for Fraud Detection. Proceedings of the Web Conference 2021, Ljubljana, 19-23 April 2021, 3168-3177. https://doi.org/10.1145/3442381.3449989
Kshetri, N. and Voas, J. (2018) Blockchain-Enabled E-Voting. IEEE Software , 35, 95-99. https://doi.org/10.1109/ms.2018.2801546
Chawla, N.V., Bowyer, K.W., Hall, L.O. and Kegelmeyer, W.P. (2002) SMOTE: Synthetic Minority Over-Sampling Technique. Journal of Artificial Intelligence Research , 16, 321-357. https://doi.org/10.1613/jair.953
Saito, T. and Rehmsmeier, M. (2015) The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets. PLOS ONE , 10, e0118432. https://doi.org/10.1371/journal.pone.0118432
Lundberg, S.M. and Lee, S.I. (2017) A Unified Approach to Interpreting Model Predictions. Proceedings of the 31 st International Conference on Neural Information Processing Systems , Long Beach, 4-9 December 2017, 4768-4777.
Xu, R.H., Baracaldo, N., Zhou, Y., Anwar, A. and Ludwig, H. (2019) Hybrid- α : An Efficient Approach for Privacy-Preserving Federated Learning. Proceedings of the 12 th ACM Workshop on Artificial Intelligence and Security , London, 15 November 2019, 13-23.