MACHINE LEARNING PARADIGMS IN BANKING AND FINANCE: TRANSFORMING RISK ASSESSMENT, FRAUD DETECTION, AND CUSTOMER INTELLIGENCE FOR SUSTAINABLE ECONOMIC GROWTH
Main Article Content
Rishabh Vinod Kumar Dubey
Dr. Ravinder Singh Madhan
The integration of machine learning (ML) into banking and financial services represents one of the most consequential technological transformations of the twenty-first century. This paper presents a comprehensive, multi-dimensional analysis of ML applications across five core banking domains: credit risk modelling, real-time fraud detection, algorithmic trading, customer relationship management (CRM), and regulatory compliance (RegTech). Drawing on a systematic literature review of 187 peer-reviewed studies published between 2015 and 2025—supplemented by empirical data from 34 global financial institutions spanning North America, Europe, Southeast Asia, and the Gulf Cooperation Council—we evaluate the performance trajectories of classical statistical models against contemporary deep learning architectures including long short-term memory (LSTM) networks, transformer-based models, and graph neural networks (GNNs). Our findings demonstrate that ensemble-based ML models reduce non-performing loan (NPL) ratios by an average of 23.4%, while convolutional neural network (CNN) pipelines achieve fraud-detection precision exceeding 97.8% at sub-millisecond latency. We critically examine regulatory compliance under the EU AI Act (2024) and Basel IV, algorithmic fairness, and federated learning for cross-institutional privacy-preserving collaboration. The paper additionally maps ML innovation onto the green economics agenda ESG scoring, green bond verification, and climate-risk stress testing themes central to the ICHSGEET mandate. We conclude with a forward-looking roadmap identifying quantum-ML hybridisation, causal inference, and large language models as the next frontier of financial intelligence.
Accenture. (2024). Banking technology vision 2024: The era of AI sovereignty. Accenture Research.
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589–609.
Bank for International Settlements. (2023). Artificial intelligence and machine learning in financial services. BIS Working Papers No. 1134.
Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71–111.
Berg, F., Kolbel, J. F., & Rigobon, R. (2022). Aggregate confusion: The divergence of ESG ratings. Review of Finance, 26(6), 1315–1344.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
Carcillo, F., Dal Pozzolo, A., Le Borgne, Y. A., Caelen, O., Mazzer, Y., & Bontempi, G. (2019). Scarff: A scalable framework for streaming credit card fraud detection with Spark. Information Fusion, 41, 182–194.
Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD, 785–794.
Cheng, H. T., et al. (2016). Wide & deep learning for recommender systems. Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, 7–10.
Climate Bonds Initiative. (2025). Green bond market summary 2024. CBI Report.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.
Dal Pozzolo, A., Caelen, O., Johnson, R. A., & Bontempi, G. (2015). Calibrating probability with undersampling for unbalanced classification. IEEE Symposium Series on Computational Intelligence, 159–166.
Deng, Y., Bao, F., Kong, Y., Ren, Z., & Dai, Q. (2017). Deep direct reinforcement learning for financial signal representation and trading. IEEE Transactions on Neural Networks and Learning Systems, 28(3), 653–664.
Duan, J., et al. (2021). Pandemic impacts on financial institutions and ML model performance: Evidence from COVID-19. Journal of Banking & Finance, 130, 106215.
European Banking Authority. (2023). Guidelines on internal governance and model risk management (EBA/GL/2023/05). EBA.
European Commission. (2024). Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Official Journal of the European Union.
FATF. (2023). Opportunities and challenges of new technologies for AML/CFT. Financial Action Task Force.
Financial Stability Board. (2024). Artificial intelligence and machine learning in financial services: Progress report. FSB.
Hand, D. J., & Henley, W. E. (1997). Statistical classification methods in consumer credit scoring: A review. Journal of the Royal Statistical Society: Series A, 160(3), 523–541.
Innan, N., et al. (2024). Financial fraud detection: A comparative study of quantum machine learning models. arXiv preprint arXiv:2403.00229.
Khandani, A. E., Kim, A. J., & Lo, A. W. (2010). Consumer credit-risk models via machine-learning algorithms. Journal of Banking & Finance, 34(11), 2767–2787.
Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30–37.
Kvamme, H., Sellereite, N., Aas, K., & Sjursen, S. (2018). Predicting mortgage default using convolutional neural networks. Expert Systems with Applications, 102, 207–217.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
Liang, D., Krishnan, R. G., Hoffman, M. D., & Jebara, T. (2018). Variational autoencoders for collaborative filtering. Proceedings of The Web Conference 2018, 689–698.
Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30.
McKinsey Global Institute. (2024). The state of AI in 2024: Generative AI's breakout year. McKinsey & Company.
Mnih, V., et al. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529–533.
Network for Greening the Financial System. (2023). NGFS climate scenarios for central banks and supervisors (3rd ed.). NGFS Secretariat.
Quinlan, J. R. (1993). C4.5: Programs for machine learning. Morgan Kaufmann.
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, 1135–1144.
Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215.
Thomson Reuters. (2023). Cost of compliance 2023: Shaping the future. Thomson Reuters Regulatory Intelligence.
Vaswani, A., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
Webersinke, N., Kraus, M., Bingler, J. A., & Leippold, M. (2022). ClimateBERT: A pretrained language model for climate-related text. AAAI 2022 Workshop on AI for Social Good.
World Bank. (2023). The global findex database 2023. World Bank Group.
World Economic Forum. (2024). The future of jobs report 2025. WEF.
Yang, Y., Morillo, I. G., & Hospedales, T. M. (2022). Deep neural decision trees. IEEE International Conference on Data Mining Workshops, 400–408.
Zheng, L., Liu, G., Yan, C., & Jiang, C. (2023). Transaction fraud detection via an adaptive graph attention network. Expert Systems with Applications, 229, 120441.
Zhou, C., Liu, F., Liu, W., Liu, J., & Gao, J. (2021). Adversarial attack on graph structured data for anti-money laundering. Proceedings of the 30th ACM CIKM, 4530–4539.
