Machine Learning in Banking and Finance: A Systematic Analysis of Applications, Model Performance, Ethical Risk, and Regulatory Readiness
Keywords:
Machine Learning, Banking and Finance, Credit Risk Assessment, Fraud Detection, Algorithmic Trading, Explainable AI, Regulatory Compliance, Financial Technology.Abstract
The banking and finance industry has entered a period of accelerated transformation as machine learning (ML) methods migrate from experimental pilots into core decision-making infrastructure. This paper presents an in-depth, literature-grounded analysis of how machine learning is reshaping credit risk assessment, fraud detection and prevention, algorithmic trading and portfolio management, and customer engagement in retail and commercial banking. Drawing on a structured review of foundational and contemporary studies, the paper traces the evolution of analytics maturity in finance from descriptive and predictive analytics toward prescriptive and real-time decision-making, and synthesizes comparative evidence on the performance of classical and modern ML architectures for credit scoring. The paper further examines the ethical, regulatory, and interpretability challenges that accompany ML adoption, including algorithmic bias, fairness, data privacy under regimes such as the GDPR, and capital-adequacy compliance under Basel III. A mixed-method research design is proposed and illustrated with representative tables and figures summarizing application-level adoption patterns, thematic distribution of the literature, and comparative model performance metrics. The analysis identifies persistent research gaps in interdisciplinary methodology, bias mitigation, real-time trading risk, model explainability, and financial inclusion, and concludes with a research agenda intended to guide the next phase of responsible machine learning adoption in banking and finance.
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References
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609.
Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and machine learning. fairmlbook.org.
Bellini, M., Romagnoli, S., & Bassis, S. (2017). Credit risk assessment in the presence of imprecision and uncertainty: A machine learning approach. Expert Systems with Applications, 69, 64–74.
Bialecki, M., Bruns, B., & Grzegorczyk, M. (2018). Big data in finance: Data-driven financial services. Palgrave Macmillan.
D'Ambrosio, A., Leporini, B., & Marozzo, F. (2018). A two-step neural network approach to customer segmentation. Expert Systems with Applications, 95, 218–231.
Finck, M. (2019). Regulatory sandbox and data protection impact assessments under the GDPR: Avoiding regulation on a “one-size-fits-none” basis. International Data Privacy Law, 9(2), 123–138.
Holmquist, L., Hannay, M., & Bunde, P. (2019). Deep learning for fraud detection: A case study. Expert Systems with Applications, 129, 262–275.
Hsieh, T. C., Ma, C. L., Lee, C. H., & Chen, H. M. (2020). Deep learning for credit scoring and its risk management. Applied Soft Computing, 96, 106658.
Kroll, J. A., Hughes, D. R., & Barocas, S. (2020). Accounting for fairness in debt collection. Proceedings of the Conference on Fairness, Accountability, and Transparency, 45–54.
Lipton, Z. C., & Steinhardt, J. (2018). Troubling trends in machine learning scholarship. arXiv:1807.03341.
Müller, S., Remy, S., & Buja, G. (2018). Credit scoring with random forests. Quantitative Finance, 18(4), 657–672.
Pratap, A., Bhatia, A., & Shaw, A. (2018). Chatbots in financial services: A comprehensive review. Journal of Marketing Communications, 26(1), 65–86.
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should I trust you?” Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144.
Schwenk, R., Tappert, R., Farkas, R., & Rist, C. (2018). A practical application of machine learning to operational risk: Going beyond the modeling illusion. The Journal of Operational Risk, 13(3), 75–92.
Shen, C., Wang, Z., Xu, S., & Wu, Y. (2020). A survey of machine learning in big data processing. ACM Computing Surveys (CSUR), 52(4), 1–40.
Todorovic, S., Sankovic, M., Pocajt, V., & Peric, N. (2019). Basel III accord and advanced machine learning models for bank credit risk assessment. Sustainability, 11(9), 2543.
Tsantekidis, A., Passalis, N., Tefas, A., & Kanniainen, J. (2017). Forecasting stock prices from the limit order book using convolutional neural networks. Expert Systems with Applications, 82, 197–205.
