Intelligent Systems, Machine Learning, and Deep Learning Algorithms for Detecting Banking Fraud: A Review

The increase in unauthorized remote banking fraud has intensified with the expansion of digital channels, creating new risks and highlighting the inadequacy of traditional methods based on fixed rules and manual audits. This review aims to synthesize recent scientific evidence on the use of machine learning and deep learning techniques for the early detection of fraudulent banking transactions, considering supervised and unsupervised models and deep architectures that allow the analysis of complex patterns present in financial transactions. A total of 357 original articles were identified in the Scopus and Web of Science databases, in addition to manual research, published up to 2025. Of these, 35 studies met the inclusion criteria established using the PICOT approach and the PRISMA protocol. The most widely implemented models in the selected studies were Random Forest, XGBoost, SVM, LSTM networks, and graph-based approaches. The combination of different algorithms improves fraud detection by integrating temporal, relational, and behavioral patterns. Advanced models show better metrics in accuracy, recall, and F1-score compared to traditional methods, expanding the possibilities for continuous monitoring and reducing false positives. There are consistent associations between the application of advanced models, the availability of quality data, and the ability to adapt to different transactional scenarios, which favor timely fraud detection if challenges such as class imbalance, the need for real-time decisions, and the heterogeneity of financial contexts are addressed. The integration of multiple approaches and the optimization of preprocessing and evaluation processes allow us to move toward more robust, scalable anti-fraud systems that are better suited to the current demands of the digital environment.

@article{Vazallo-Bautista2025,
  title     = {Intelligent Systems, Machine Learning, and Deep Learning Algorithms for Detecting Banking Fraud: A Review},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Jessica Vazallo-Bautista and Allison Villalobos-Peña and Juan Soria-Quijaite},
  doi       = {10.14569/IJACSA.2025.0161266},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161266}
}