eISSN: 3087-4262 editor@aimjournals.com
All Journals
Submit your article

International Journal of Intelligent Data and Machine Learning

Open Access Peer Review International
Open Access

A Systematic Review of Machine Learning Approaches For AI-Driven Fraud Detection in Loyalty Programs

DOI https://doi.org/10.55640/ijidml-v03i04-01
PDF
Igor Litovsky 1 ,
4 Founder & CTO, Mastermind Loyalty Toronto, Canada

Abstract

This article examines machine learning approaches used for fraud detection in loyalty programs, treating loyalty abuse as a distinct analytical problem rather than a simplified extension of payment fraud. The topic is timely because contemporary loyalty ecosystems combine account-based stored value, omnichannel interaction data, partner integrations, and promotion logic, which together generate heterogeneous fraud patterns and unstable labels. The article aims to systematize the prominent model families used in fraud analytics and determine which are most suitable for loyalty-program environments. The study relies on source analysis, comparative review, conceptual synthesis, and analytical generalization. Recent research on fraud analytics, anomaly detection, graph learning, tabular modeling, behavioral biometrics, explainable artificial intelligence, and adaptive risk estimation is examined. The analytical part identifies the strongest methodological trajectories for loyalty fraud detection, including hybrid pipelines, graph-based modeling, and behavior-aware scoring. The findings apply to program operators, fraud analysts, and product teams designing AI-supported decision systems for account protection, redemption control, and abuse prevention.

Keywords

loyalty programs, fraud detection, machine learning, graph neural networks, anomaly detection, tabular learning, behavioral biometrics, explainable AI, risk scoring, promotional abuse

References

📄 Al-Hashedi, K. G., & Magalingam, P. (2021). Financial fraud detection applying data mining techniques: A comprehensive review from 2009 to 2019. Computer Science Review, 40, 100402. https://doi.org/10.1016/j.cosrev.2021.100402
📄 Arik, S. Ö., & Pfister, T. (2021). TabNet: Attentive interpretable tabular learning. Proceedings of the AAAI Conference on Artificial Intelligence, 35(8), 6679–6687. https://doi.org/10.1609/aaai.v35i8.16826
📄 Carcillo, F., Le Borgne, Y.-A., Caelen, O., Kessaci, Y., Oblé, F., & Bontempi, G. (2019). Combining unsupervised and supervised learning in credit card fraud detection. Information Sciences. Advance online publication. https://doi.org/10.1016/j.ins.2019.05.042
📄 Cheng, D., Zou, Y., Xiang, S., & Jiang, C. (2025). Graph neural networks for financial fraud detection: A review. Frontiers of Computer Science, 19(9), 199609.
📄 Gorishniy, Y., Rubachev, I., Khrulkov, V., & Babenko, A. (2021). Revisiting deep learning models for tabular data. Advances in Neural Information Processing Systems, 34, 18932–18943.
📄 Pang, G., Shen, C., Cao, L., & Van Den Hengel, A. (2021). Deep learning for anomaly detection: A review. ACM Computing Surveys, 54(2), 1–38.
📄 Papaioannou, M., Pelekoudas-Oikonomou, F., Mantas, G., Serrelis, E., Rodriguez, J., & Fengou, M.-A. (2023). A survey on quantitative risk estimation approaches for secure and usable user authentication on smartphones. Sensors, 23(6), 2979. https://doi.org/10.3390/s23062979
📄 Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2(3), 160. https://doi.org/10.1007/s42979-021-00592-x
📄 Vilone, G., & Longo, L. (2021). Notions of explainability and evaluation approaches for explainable artificial intelligence. Information Fusion, 76, 89–106. https://doi.org/10.1016/j.inffus.2021.05.009
📄 Zhang, J., & Wang, Y. (2024). A survey of behavioral biometric authentication on smartphones. In Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application (ICMLCA ’23) (pp. 722–729). Association for Computing Machinery. https://doi.org/10.1145/3650215.3650342