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Liburkina L. M.

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Integration of Machine Learning Techniques in Bank Fraud Detection and Prevention
Caprian Iurie

Caprian, Iurie. (2025) “Integration of Machine Learning Techniques in Bank Fraud Detection and Prevention.” Business Inform 12:39–39.
https://doi.org/10.32983/2222-4459-2025-12-39-39

Section: Finance, Money Circulation and Credit

Article is written in English
Downloads/views: 0

UDC 004.8:336.71

Abstract:
The rapid expansion of digital banking services has significantly increased the exposure of financial institutions to various forms of fraud, including payment fraud, identity theft, and account takeover. Traditional rule-based fraud detection systems are increasingly ineffective in managing high-volume, high-velocity transactional data and adapting to evolving fraud patterns. In this context, machine learning (ML) has emerged as a key technological solution for enhancing fraud detection and prevention capabilities. This article examines the application of machine learning techniques in bank fraud detection, with a focus on supervised, unsupervised, and hybrid approaches. Supervised models leverage labeled historical data to identify known fraud patterns, while unsupervised methods detect anomalies in unlabeled datasets. Hybrid approaches combine both strategies to improve robustness and adaptability. The analysis draws on recent academic literature and practical implementations within the banking sector. The results indicate that ML-based systems are efficient in identifying anomalous transactions, reducing false positives, and improving overall operational efficiency. Additionally, these systems support regulatory compliance by enabling continuous monitoring and more accurate risk assessment. However, several challenges remain, including data quality and imbalance, algorithmic bias, model explainability, and the integration of ML solutions into legacy banking infrastructures. To address these issues, the article proposes a structured framework for implementing ML-driven fraud detection systems, emphasizing data governance, model transparency, and alignment with regulatory requirements. The study provides actionable insights for researchers and practitioners seeking to design scalable, reliable, and ethically responsible fraud detection solutions in modern digital banking environments.

Keywords: machine learning, bank fraud detection, anomaly detection, predictive modeling, financial AI.

Fig.: 3. Tabl.: 1. Bibl.: 20.

Caprian Iurie – Postgraduate Student, State University of Moldova (60 Alexei Mateevici Str., Chisinau, Moldova)
Email: [email protected]

List of references in article

Abdallah A., Maarof M. A. & 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
Almalki F. & Masud M. (2025). Financial fraud detection using explainable AI and stacking ensemble methods. arXiv. https://doi.org/10.48550/arXiv.2505.10050
Bahnsen A. C., Aouada D., Stojanovic A. & Ottersten B. (2016). Feature engineering strategies for credit card fraud detection. Expert Systems with Applications, 51, 134–142. https://doi.org/10.1016/j.eswa.2015.12.030
Caprian I. (2024). Adaptive supervised and unsupervised ML approaches for banking fraud prevention. International Journal of Banking Technology, 1(2), 40–55.
Caprian I. (2023). Hybrid machine learning frameworks for anomaly detection in financial transactions. Journal of Financial AI Research, 2(1), 15–28.
Carcillo F., Le Borgne Y. A., Caelen O. & Bontempi G. (2019). Scarcity and concept drift in fraud detection: Experimental evaluation of online learning algorithms. Neurocomputing, 348, 9–20.
Chennuri S. (2024). Advancing fraud detection in banking: Integration of data pipelines, machine learning, and cloud computing. International Journal for Multidisciplinary Research, 6(6). https://doi.org/10.36948/ijfmr.2024.v06i06.29893
Dal Pozzolo A., Caelen O. & Le Borgne Y. A. et al. (2015). Learned lessons in credit card fraud detection from a practitioner perspective. Expert Systems with Applications, 10(41), 4915–4928. https://doi.org/10.1016/j.eswa.2014.02.026
Hafez I. Y., Hafez A. Y. & Saleh A. et al. (2025). A systematic review of AI-enhanced techniques in credit card fraud detection. Journal of Big Data, 12, Art. 6. https://doi.org/10.1186/s40537-024-01048-8
Ihsan H. (2024). AI-driven evolution of fraud detection in digital banking. Journal of Social Sciences and Humanities Archives, 1(1), 9–18.
Ismail M. M. & Haq M. A. (2024). Enhancing enterprise financial fraud detection using machine learning. Engineering, Technology & Applied Science Research, 4(14), 14854–14861. https://doi.org/10.48084/etasr.7437
Kacheru G., Bajjuru R. & Arthan N. (2025). Artificial intelligence in finance: Predictive analytics, fraud detection, and risk management in 2024. Formosa Journal of Science and Technology, 1(4), 141–154. https://doi.org/10.55927/fjst.v4i1.13398
Mishra B. R., Gadasandula K. & Saini G. et al. (2025). The role of artificial intelligence in fraud detection and prevention in banking. Journal of Information Systems Engineering and Management, 49s(10), 1167–1173. https://doi.org/10.5281/jisem.v10i49s.10061
Ngai E. W. T., Hu Y. & Wong Y. H. et al. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 3(50), 559–569. https://doi.org/10.1016/j.dss.2010.08.006
Nobel S. M. N., Sultana S. & Singha S. P. et al. (2024). Unmasking banking fraud: Unleashing the power of machine learning and explainable AI (XAI) on imbalanced data. Information, 6(15), Art. 298. https://doi.org/10.3390/info15060298
Phua C., Lee V., Smith K. & Gayler R. (2010). A comprehensive survey of data mining based fraud detection research. Artificial Intelligence Review, 1(34), 1–14. https://doi.org/10.48550/arXiv.1009.6119
Rojan Z. & Adnan M. (2024). Financial fraud detection based on machine and deep learning: A review. Indonesian Journal of Computer Science, 3(13), 4366–4389. https://doi.org/10.33022/ijcs.v13i3.4059
Talukder M. A., Hossen R. & Uddin M. A. et al. (2024). Securing transactions: A hybrid dependable ensemble machine learning model using IHT-LR and grid search. Cybersecurity, 7, Art. 32. https://doi.org/10.1186/s42400-024-00221-z
West J. & Bhattacharya M. (2016). Intelligent financial fraud detection: A comprehensive review. Computers & Security, 57, 47–66. https://doi.org/10.1016/j.cose.2015.09.005
Xu B., Wang Y., Liao X. & Wang K. (2023). Efficient fraud detection using deep boosting decision trees. Decision Support Systems, 175. https://doi.org/10.1016/j.dss.2023.114037

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