Machine Learning-Based Fraud Detection in Mobile Banking Applications
Keywords:
machine learning, fraud detection, mobile banking, ensemble learning, deep learning, explainable artificial intelligence, federated learning, anomaly detection, financial technology, transaction monitoring, feature engineering, mobile moneyAbstract
The mobile channel has emerged as the predominant channel for retail financial transactions, driven by a surge in adoption during the pandemic and increased penetration of smartphones [19]. Along with this growth in volume, fraud at the transaction level, such as account takeover, unauthorized transfers, and other schemes that are specific to mobile-money services, like SIM swapping and agent-assisted diversion, has also increased [3], [8], [13]. This paper collates the results of 24 sources over the last two decades (up to 2024) to explore the use of machine learning (ML) techniques in detecting fraud in mobile banking and related payment systems. The synthesis includes classical supervised algorithms like random forest, support vector machines and gradient boosting [1, 4, 22]; deep learning architectures like long short-term memory (LSTM) networks with attention mechanisms and graph neural networks [6, 15]; natural language processing and transformer-based models that can be used for unstructured transaction narratives [9, 24] and ensemble and hybrid frameworks reported to outperform individual base learners [11, 21]. Explainable AI (XAI) and Federated learning are analyzed as converging solutions to the demands for regulatory transparency and data-privacy constraints in distributed banking infrastructures [5], [17]. In the literature surveyed, the reported detection accuracy varies widely from around 85% for baseline logistic regression models to more than 98% in optimized ensemble and gradient boosted models, several of which point to recall-precision trade-offs that are of practical significance in imbalanced fraud datasets [1, 4, 11, 22]. The paper also examines the practices of transaction monitoring for AML compliance [18] and adoption trends of mobile banking apps [19] as well as computational and regulatory hurdles to real-time deployment. The results show that the most promising path forward for mobile banking fraud detection to be both scalable and trustworthy are hybrid, explainable, and privacy-preserving architectures up to 2024.
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