Leveraging Artificial Intelligence and Data Analytics for Enhancing Efficiency, Fraud Detection and Early Risk Prediction in the Banking Sector

Abstract

The rapid digitalization of banking has positioned Artificial Intelligence (AI) and Data Analytics as key enablers of operational innovation. This paper examines how AI technologies, including machine learning and predictive analytics, enhance efficiency, detect fraud and predict financial risks in the banking sector. By analysing credit scoring, transaction monitoring and customer behaviour, the study highlights how data-driven systems improve decision-making while reducing manual errors and operational costs. A mixed-method approach combining quantitative analysis and case studies reveals significant gains in performance and risk mitigation for AI-adopting banks. Despite these benefits, challenges such as data privacy and model transparency persist. The paper emphasizes the need for responsible AI integration to build a resilient and secure financial ecosystem.