Predictive Analytics in Banking: Harnessing AI and Cloud Computing for Smarter Decisions
Ketan Modi1, Dushyantkumar Nakrani2
1Ketan Modi, Department of Banking, Bank of America, Princeton (New Jersey), United States of America (USA).
2Dushyant Nakrani, Department of Banking, Bank of America, Princeton (New Jersey), United States of America (USA).
Manuscript Received on 30 May 2025 | First Revised Manuscript Received on 10 June 2025 | Second Revised Manuscript Received on 22 June 2025 | Manuscript Accepted on 15 July 2025 | Manuscript published on 30 July 2025 | PP: 15-20 | Volume-15 Issue-3, July 2025 | Retrieval Number: 100.1/ijsce.G110814070625 | DOI: 10.35940/ijsce.G1108.15030725
Open Access | Editorial and Publishing Policies | Cite | Zenodo | OJS | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Artificial Intelligence (AI), Cloud Computing, Predictive Analytics, Decision-Making, Banking, Risk Management, Fraud Detection, Machine Learning, Customer Experience, Financial Services.
Scope of the Article: Artificial Intelligence