Integrating business intelligence and predictive analytics in banking: A framework for optimizing financial decision-making

Abstract

Integrating Business Intelligence (BI) and predictive analytics in banking has become a pivotal strategy for optimizing financial decision-making. This review paper explores this integration's theoretical foundations, current trends, and challenges while proposing a comprehensive framework to enhance decision-making processes. The proposed framework emphasizes the importance of a unified data architecture, advanced data integration techniques, and the synergistic use of BI tools and predictive models. By examining the implications for banking institutions, the paper highlights how this integration can transform strategic and operational decisions, improve risk management, and drive personalized customer engagement. Additionally, future trends such as the adoption of AI, real-time analytics, and enhanced data governance are discussed, along with recommendations for banks aiming to implement this integration successfully. The findings underscore the transformative potential of combining BI and predictive analytics to foster innovation, efficiency, and competitive advantage in the banking sector. Keywords: Business Intelligence (BI), Predictive Analytics, Banking Sector, Financial Decision-Making, Data Integration.