Artificial Intelligence in Banking: Applications, Opportunities, Risks, and Future Directions
Abstract
Artificial Intelligence (AI) has become an important technological driver of transformation across the banking and financial services sector. Financial institutions increasingly use AI and Machine Learning (ML) to automate processes, improve customer experiences, manage risks, detect fraud, and support data-driven decision-making. This review examines the major applications of AI in banking, including credit assessment, fraud detection, customer segmentation, algorithmic trading, anti-money laundering, regulatory compliance, and intelligent customer service. The study compares traditional machine learning approaches with more advanced deep learning and natural language processing techniques used in financial applications. Particular attention is given to the increasing use of generative AI and large language models in banking operations, including document analysis, knowledge management, and customer support. The review also examines challenges related to data privacy, cybersecurity, algorithmic bias, model explainability, regulatory compliance, and operational risk. Furthermore, the paper discusses the importance of responsible and trustworthy AI frameworks for financial institutions. Emerging research directions involving federated learning, explainable AI, synthetic data, and AI governance are examined. The review concludes that successful AI adoption in banking requires a balance between technological innovation, regulatory oversight, security, transparency, and human supervision.
References
Altman, E. I., Marco, G., & Varetto, F. (1994). Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks. Journal of Banking & Finance, 18(3), 505–529. https://doi.org/10.1016/0378-4266(94)90007-8
Arner, D. W., Barberis, J. N., & Buckley, R. P. (2016). The evolution of FinTech: A new post-crisis paradigm? Georgetown Journal of International Law, 47(4), 1271–1319.
Sandra, K. (2024). Large language models for data catalog enrichment: A survey with operational evidence from enterprise deployments. International Journal of Computer Science Engineering Techniques, 12(3), 1–8.
Sandra, K. (2022). Intelligent data workbench design for multi-language code compatibility. International Journal of Computer Science Engineering Techniques, 12(1), 1–17.
Sandra, K. (2024). The regulated banking AI lakehouse. Indo-Continental Academic Publishers.
Baesens, B., Van Gestel, T., Viaene, S., Stepanova, M., Suykens, J., & Vanthienen, J. (2003). Benchmarking state-of-the-art classification algorithms for credit scoring. Journal of the Operational Research Society, 54(6), 627–635. https://doi.org/10.1057/palgrave.jors.2601545
Bussmann, N., Giudici, P., Marinelli, D., & Papenbrock, J. (2021). Explainable machine learning in credit risk management. Computational Economics, 57, 203–216. https://doi.org/10.1007/s10614-020-10042-0
Dorfleitner, G., Hornuf, L., Schmitt, M., & Weber, M. (2017). FinTech in Germany. Springer. https://doi.org/10.1007/978-3-319-54666-7
Fuster, A., Goldsmith-Pinkham, P., Ramadorai, T., & Walther, A. (2022). Predictably unequal? The effects of machine learning on credit markets. The Journal of Finance, 77(1), 5–47. https://doi.org/10.1111/jofi.13090
Huang, Z., Chen, H., Hsu, C.-J., Chen, W.-H., & Wu, S. (2004). Credit rating analysis with support vector machines and neural networks: A market comparative study. Decision Support Systems, 37(4), 543–558. https://doi.org/10.1016/S0167-9236(03)00086-1
Lessmann, S., Baesens, B., Seow, H.-V., & Thomas, L. C. (2015). Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research. European Journal of Operational Research, 247(1), 124–136. https://doi.org/10.1016/j.ejor.2015.05.030
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 an academic review of literature. Decision Support Systems, 50(3), 559–569. https://doi.org/10.1016/j.dss.2010.08.006
Ozbayoglu, A. M., Gudelek, M. U., & Sezer, O. B. (2020). Deep learning for financial applications: A survey. Applied Soft Computing, 93, 106384. https://doi.org/10.1016/j.asoc.2020.106384
Philippon, T. (2016). The fintech opportunity (NBER Working Paper No. 22476). National Bureau of Economic Research. https://doi.org/10.3386/w22476
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012
Wamba-Taguimdje, S.-L., Fosso Wamba, S., Kamdjoug, J. R. K., & Tchatchouang Wanko, C. E. (2020). Influence of artificial intelligence (AI) on firm performance: The business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893–1924. https://doi.org/10.1108/BPMJ-10-2019-0411
Kou, Y., Lu, C.-T., Sirwongwattana, S., & Huang, Y.-P. (2004). Survey of fraud detection techniques. IEEE International Conference on Networking, Sensing and Control, 749–754. https://doi.org/10.1109/ICNSC.2004.1297040
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
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
Paruchuri, J. K. (2021). Lakehouse architecture: Unifying data lakes and data warehouses. International Journal of Computer Techniques (IJCT), 8(1), 1–15.
Paruchuri, J. K. (2021). Exactly-once semantics in distributed stream processing at scale. International Journal of Computer Science Engineering Techniques (IJCSE), 5(1), 1–15.
Paruchuri, J. K. (2023). Design and performance evaluation of an elastic multi-tenant Spark SQL platform using Apache Kyuubi on Kubernetes. Journal of Advanced Research in Technology and Management Sciences, 5(3), 33–45.
He, H., Zhang, W., & Zhang, S. (2018). A novel ensemble method for credit scoring: Adaption of different imbalance ratios. Expert Systems with Applications, 98, 157–168. https://doi.org/10.1016/j.eswa.2018.01.012
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. https://doi.org/10.1016/j.jbankfin.2010.06.001
Sirignano, J., Sadhwani, A., & Giesecke, K. (2018). Deep learning for mortgage risk. Journal of Financial Econometrics, 16(3), 514–542. https://doi.org/10.1093/jjfinec/nby004
Leo, M., Sharma, S., & Maddulety, K. (2019). Machine learning in banking risk management: A literature review. Risks, 7(1), 29. https://doi.org/10.3390/risks7010029
