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RESEARCH CENTER FOR INDUSTRIAL DEVELOPMENT PROBLEMS of NAS of Ukraine (KHARKIV, UKRAINE)

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Liburkina L. M.

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Machine Learning for Fraud Detection in Digital Banking and DeFi: Towards Sustainable Financial Systems
Caprian Iurie, Caprian I., Lom I. V.

Caprian, Iurie., Caprian, Iulia., and Lom, Ionut Valentin. (2026) “Machine Learning for Fraud Detection in Digital Banking and DeFi: Towards Sustainable Financial Systems.” Business Inform 8:96–106.
https://doi.org/10.32983/2222-4459-2026-8-96-106

Section: Information Technologies in the Economy

Article is written in English
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UDC 336.7:343:5:004.8

Abstract:
The rapid development of digital banking and decentralized finance (DeFi) has increased the complexity of financial fraud detection. Although machine learning methods are increasingly applied to fraud detection, the literature remains fragmented regarding the comparative performance of different algorithms under highly imbalanced conditions and the implications of their application for decentralized and sustainable financial systems.The aim of this study is to comparatively evaluate selected supervised machine learning models for financial fraud detection and to examine the implications of their application for digital banking, DeFi, and sustainable financial systems.The study uses the publicly available Credit Card Fraud Detection dataset containing anonymized financial transactions with a highly imbalanced distribution between legitimate and fraudulent observations. Three supervised machine learning algorithms were comparatively evaluated: Logistic Regression, Random Forest, and XGBoost. Data preparation and model evaluation were implemented in Python using the Scikit-learn and XGBoost libraries. Model performance was assessed using precision, recall, F1-score, and ROC-AUC.The comparative results indicate that the ensemble-based models, particularly XGBoost, achieved stronger overall performance than Logistic Regression across the selected evaluation metrics. Random Forest also demonstrated competitive performance, supporting the applicability of ensemble approaches to fraud detection in complex and highly imbalanced financial data.The scientific contribution of the study consists in the comparative assessment of three widely used machine learning approaches within a unified fraud detection framework and in linking the obtained findings to the emerging challenges of DeFi and sustainable financial systems. The study also discusses the role of explainable artificial intelligence (XAI) in improving transparency and interpretability in financial machine learning applications.The findings may support financial institutions and digital financial service providers in selecting appropriate machine learning approaches for fraud detection and in balancing predictive performance with transparency and operational requirements. The discussion of DeFi highlights the need for adaptive analytical approaches capable of addressing the specific risks of decentralized financial ecosystems.The study concludes that machine learning, particularly ensemble-based approaches, has significant potential to strengthen fraud detection and contribute to safer, more resilient, and sustainable digital financial systems.

Keywords: machine learning; fraud detection; FinTech; DeFi; blockchain; explainable artificial intelligence; sustainability.

Fig.: 2. Tabl.: 2. Bibl.: 28.

Caprian Iurie – Postgraduate Student, Department of Business and Administration, State University of Moldova (60 Alexei Mateevici Str., Chisinau, Moldova)
Email: [email protected]
Caprian Iulia – PhD, Associate Professor, Faculty of Economics and Legal Sciences, State University of Moldova (60 Alexei Mateevici Str., Chisinau, Moldova)
Email: [email protected]
Lom Ionut Valentin – Research Associate, Department of Finance and Banking, National Institute for Aerospace Research «Elie Carafoli» (INCAS) (sector 6, 220 Iuliu Maniu Blvd., Bucharest, 061126, Romania)
Email: [email protected]

List of references in article

Aggarwal C. C. (2017). Outlier Analysis. 2nd ed. Cham: Springer. https://doi.org/10.1007/978-3-319-47578-3
Ahmed M., Mahmood A. N. & Hu J. (2016). A Survey of Network Anomaly Detection Techniques. Journal of Network and Computer Applications, 60, 19–31. https://doi.org/10.1016/j.jnca.2015.11.016
Allen F., Gu X. & Jagtiani J. (2021). A Survey of FinTech Research and Policy Discussion. Review of Corporate Finance, 3–4(1), 259–339. https://doi.org/10.1561/114.00000008
Arner D. W., Barberis J. & Buckley R. P. (2018). FinTech and RegTech: Impact on Regulators and Banks. Journal of Banking Regulation, 4(19), 329–344. https://doi.org/10.1057/s41261-017-0037-1
Bhattacharyya S., Jha S., Tharakunnel K. & Westland J. C. (2011). Data Mining for Credit Card Fraud: A Comparative Study. Decision Support Systems, 3(50), 602–613. https://doi.org/10.1016/j.dss.2010.08.008
Brown T., Mann B., Ryder N. & et al. (2020). Language Models are Few Shot Learners. Advances in Neural Information Processing Systems. https://doi.org/10.48550/arXiv.2005.14165
Caprian I. (2025). A Hybrid Modular Architecture for Fraud Detection Using Offline and Online Machine Learning Models. The Problems of Economy, 3, 312–320. https://doi.org/10.32983/2222-0712-2025-3-312-320
Carcillo F., Le Borgne Y. A., Caelen O. & 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
Chen T. & Guestrin C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). https://doi.org/10.1145/2939672.2939785
Cong L. W. & He Z. (2019). Blockchain Disruption and Smart Contracts. Review of Financial Studies, 5(32), 1754–1797. https://doi.org/10.1093/rfs/hhz007
Dal Pozzolo A., Boracchi G., Caelen O., Alippi C. & Bontempi G. (2018). Credit Card Fraud Detection: A Realistic Modeling and a New Learning Strategy. IEEE Transactions on Neural Networks and Learning Systems, 8(29), 3784–3797. https://doi.org/10.1109/TNNLS.2017.2736643
Ertam F. (2025). Detecting Ethereum Fraud in Near Real Time Using Explainable Artificial Intelligence in Blockchain Networks. Applied Sciences, 15, Art. 10841. https://doi.org/10.3390/app152110841
Esteva A., Kuprel B., Novoa R. A. & et al. (2017). Dermatologist Level Classification of Skin Cancer with Deep Neural Networks. Nature, 542, 115–118. https://doi.org/10.1038/nature21056
Fiore U., De Santis A., Perla F., Zanetti P. & Palmieri F. (2019). Using Generative Adversarial Networks to Improve Classification Effectiveness in Credit Card Fraud Detection. Information Sciences, 479, 448–455. https://doi.org/10.1016/j.ins.2017.12.030
Ghnemat R. & Mosa H. (2025). Blockchain Based Fraud Detection: A Systematic Review of Ethereum Network Applications. Cluster Computing, 28, Art. 1080. https://doi.org/10.1007/s10586-025-04962-9
Goodfellow I., Bengio Y. & Courville A. (2016). Deep Learning. Cambridge, MA: MIT Press. https://www.deeplearningbook.org
Kingma D. P. & Welling M. (2013). Auto Encoding Variational Bayes. arXiv preprint. arXiv:1312.6114. https://arxiv.org/abs/1312.6114
Kourtney Y., Lu C. T., Sirwongwattana S. & Huang Y. P. (2004). Survey on Fraud Detection Techniques. Proceedings of the IEEE International Conference on Networking, Sensing and Control (pp. 749–754). https://doi.org/10.1109/ICNSC.2004.1297040
Kshetri N. (2017). Blockchain's Roles in Strengthening Cybersecurity and Protecting Privacy. Telecommunications Policy, 10(41), 1027–1038. https://doi.org/10.1016/j.telpol.2017.09.003
Lundberg S. M., Erion G. & Lee S. I. (2018). Consistent Individualized Feature Attribution for Tree Ensembles. arXiv preprint. arXiv:1802.03888. https://arxiv.org/abs/1802.03888
Lundberg S. M. & Lee S. I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems (NeurIPS) (pp. 4765–4774). https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions
McNally S., Roche J. & Caton S. (2018). Predicting the Price of Bitcoin Using Machine Learning. 26th Euromicro International Conference on Parallel, Distributed and Network Based Processing (PDP). https://doi.org/10.1109/PDP2018.2018.00060
Nakamoto S. (2008). Bitcoin: A Peer to Peer Electronic Cash System. https://bitcoin.org/bitcoin.pdf
Ngai E. W. T., Hu Y., Wong Y. H., Chen Y. & Sun X. (2011). The Application of Data Mining Techniques in Financial Fraud Detection: A Classification Framework and Academic Review of Literature. Decision Support Systems, 3(50), 559–569. https://doi.org/10.1016/j.dss.2010.08.006
Phua C., Lee V., Smith K. & Gayler R. (2010). A Comprehensive Survey of Data Mining based Fraud Detection Research. arXiv preprint. arXiv:1009.6119. https://arxiv.org/abs/1009.6119
Schar F. (2021). Decentralized Finance: On Blockchain and Smart Contract Based Financial Markets. Federal Reserve Bank of St. Louis Review, 2(103), 153–174. https://doi.org/10.20955/r.103.153-74
Weber M., Domeniconi G., Chen J., Weidele D. K., Bellei C., Robinson T. & Leiserson C. E. (2019). Anti Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks. arXiv preprint. arXiv:1908.02591. https://arxiv.org/abs/1908.02591
Zohar A. (2015). Bitcoin: Under the Hood. Communications of the ACM, 58, 104–113. https://doi.org/10.1145/2701411