Predictive Analytics in Banking: Harnessing AI and Cloud Computing for Smarter Decisions
Main Article Content
Abstract
The banking industry faces unprecedented challenges in risk management, fraud detection, customer personalisation, and operational efficiency, amid vast and complex data volumes. This paper investigates the transformative potential of integrating Predictive Analytics (PA) with Artificial Intelligence (AI) and Cloud Computing to empower smarter, data-driven decisionmaking within financial institutions. We explore how cloud platforms provide the essential, scalable, elastic, and cost-effective infrastructure necessary to process massive banking datasets (transactional, behavioural, and market) that were previously prohibitive. Concurrently, advanced AI techniques – including machine learning (ML) and deep learning (DL) – are leveraged to build sophisticated predictive models that can uncover complex patterns and generate actionable insights from this data. The research examines key applications of this synergistic trio across the banking value chain: enhancing credit scoring accuracy and default prediction, enabling real-time fraud detection and prevention, personalizing customer offerings and optimizing retention strategies, improving algorithmic trading, and streamlining operational processes. While highlighting significant benefits such as reduced financial losses, improved customer experience, increased revenue opportunities, and optimized capital allocation, the paper also critically addresses inherent challenges. These include data privacy and security concerns in the cloud, model explainability ("black box" problem) for regulatory compliance, potential algorithmic bias, and the need for robust data governance frameworks. We argue that the strategic convergence of Predictive Analytics, AI, and Cloud Computing is not merely an operational upgrade but a fundamental shift towards proactive, intelligent banking. Financial institutions that successfully navigate the challenges and harness this powerful combination will gain a decisive competitive edge through superior risk management, enhanced customer satisfaction, and sustained innovation. This paper provides a comprehensive overview of the current landscape, practical applications, benefits, and critical considerations for implementing this transformative technology stack in modern banking.
Downloads
Article Details
Section

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
How to Cite
References
Fares, O.H., Butt, I. & Lee, S.H.M. Utilization of artificial intelligence in the banking sector: a systematic literature review. J Financ Serv Mark 28, 835–852 (2023). DOI: https://doi.org/10.1057/s41264-022-00176-7
Doumpos, M., Zopounidis, C., Gounopoulos, D., Platanakis, E., & Zhang, W. (2023). Operational research and artificial intelligence methods in banking. European Journal of Operational Research, 306(1), 1-16. DOI: https://doi.org/10.1016/j.ejor.2022.04.027
Rahman, M., Ming, T. H., Baigh, T. A., & Sarker, M. (2023). Adoption of artificial intelligence in banking services: an empirical analysis. International Journal of Emerging Markets, 18(10), 4270-4300. DOI: https://doi.org/10.1108/IJOEM-06-2020-0724
Donepudi, P. K. (2017). Machine learning and artificial intelligence in banking. Engineering International, 5(2), 83-86.
DOI: https://doi.org/10.18034/ei.v5i2.490
Jakšič, M., Marinč, M. Relationship banking and information technology: the role of artificial intelligence and FinTech. Risk Manag 21, 1–18 (2019). DOI: https://doi.org/10.1057/s41283-018-0039-y
Ashta, A., & Herrmann, H. (2021). Artificial intelligence and fintech: An overview of opportunities and risks for banking, investments, and microfinance. Strategic Change, 30(3), 211-222. DOI: https://doi.org/10.1002/jsc.2404
AL-Dosari, K., Fetais, N., & Kucukvar, M. (2024). Artificial intelligence and cyber defence system for the banking industry: A qualitative study of AI applications and challenges. Cybernetics and systems, 55(2), 302-330.
DOI: https://doi.org/10.1080/01969722.2022.2112539
Choithani, T., Chowdhury, A., Patel, S. et al. A Comprehensive Study of Artificial Intelligence and Cybersecurity on Bitcoin, Crypto Currency and Banking System. Ann. Data. Sci. 11, 103–135 (2024). DOI: https://doi.org/10.1007/s40745-022-00433-5
Rodrigues, A. R. D., Ferreira, F. A., Teixeira, F. J., & Zopounidis, C. (2022). Artificial intelligence, digital transformation and cybersecurity in the banking sector: A multi-stakeholder cognition-driven framework. Research in International Business and Finance, 60, 101616. DOI: https://doi.org/10.1016/j.ribaf.2022.101616
Qasaimeh, G. M., & Jaradeh, H. E. (2022). The impact of artificial intelligence on the practical application of cyber governance
In Jordanian commercial banks. International Journal of Technology Innovation and Management (IJTIM), 2(1).
DOI: https://doi.org/10.54489/ijtim.v2i1.61
Trozze, A., Davies, T., & Kleinberg, B. (2022). Explaining prosecution outcomes for cryptocurrency-based financial crimes. Journal of Money Laundering Control, 26(1), 172–188. DOI: https://doi.org/10.1108/jmlc-10-2021-0119
Lăzăroiu, G., Bogdan, M, Geamănu, M., Hurloiu, L., Ionescu, L., & Ștefănescu, R. (2023). Artificial intelligence algorithms and cloud computing technologies in blockchain-based fintech management. Oeconomia Copernicana, 14(3), 707–730.
DOI: https://doi.org/10.24136/oc.2023.021
Xue, M., Xiu, G., Saravanan, V. and Montenegro-Marin, C.E. (2021), "Cloud computing with AI for banking and e-commerce applications", The Electronic Library, Vol. 39 No. 4, pp. 539-552.
https://www.emerald.com/insight/content/doi/10.1108/el-07-2020-0207/full/html
Velmurugan, R., Kumar, R., Saravanan, D., Patnaik, S., Ikkurthi, S.K. (2023). A Critical Cloud Security Risks Detection Using Artificial Neural Networks at Banking Sector. In: Agarwal, P., Khanna, K., Elngar, A.A., Obaid, A.J., Polkowski, Z. (eds) Artificial Intelligence for Smart Healthcare. EAI/Springer Innovations in Communication and Computing. Springer, Cham. DOI: https://doi.org/10.1007/978-3-031-23602-0_6
The Role of Artificial Intelligence in the Cybersecurity System of Banking Institutions in the Conditions of Instability / Dubyna M., Shchur R., Shyshkina O., Sadchykova I., Panchenko O., Bazilinska O. // Journal of Theoretical and Applied Information Technology. - 2024. - Vol. 102 (19). - P. 6950-6965. https://ekmair.ukma.edu.ua/handle/123456789/33555
Thisarani, M., & Fernando, S. (2021, June). Artificial intelligence for futuristic banking. In 2021 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) (pp. 1-13). IEEE. DOI: https://doi.org/10.1109/ICE/ITMC52061.2021.9570253
Gorian, E. (2021). Artificial Intelligence Technology And Investments Guarantees In Banking: The Cybersecurity Aspect. In N. Lomakin (Ed.), Finance, Entrepreneurship and Technologies in Digital Economy, vol 103. European Proceedings of Social and Behavioural Sciences (pp. 547-552). European Publisher.
DOI: https://doi.org/10.15405/epsbs.2021.03.69
X. Hu and K. Wang, "Bank Financial Innovation and Computer Information Security Management Based on Artificial Intelligence," 2020 2nd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), Taiyuan, China, 2020, pp. 572-575, DOI: https://doi.org/10.1109/MLBDBI51377.2020.00120