Integrating Cloud-Based Predictive Analytics for Strategic Decision-Making in Mid-Sized Commercial Banking: An Empirical Evaluation of Azure Synapse and Power BI

The digital transformation of banking has created new opportunities for predictive analytics to enhance decision-making, risk management, and customer engagement. This study examines the integration of Power BI and Azure Synapse Analytics within a mid-sized commercial bank to evaluate how predictive analytics supports strategic decision-making. A mixed-methods approach was employed, combining quantitative analysis of system performance with qualitative feedback from 150 banking professionals. Results show that predictive models reduced credit risk misclassification by 37%, improved customer segmentation effectiveness (cluster purity) by 42% (a move from 71.0% to 83.0% absolute accuracy), and accelerated decision-making cycles by 55%. Revenue forecasting accuracy increased by 29%, while fraud detection alert precision improved by 31%, demonstrating enhanced operational resilience. Correlation analysis revealed strong positive relationships between predictive accuracy and managerial confidence (r = 0.64, p < 0.01), as well as between visualization clarity and strategic agility (r = 0.58, p < 0.01). The findings highlight the importance of integrated cloud-based analytics platforms in consolidating fragmented data and delivering real-time insights. This study contributes empirical evidence that integrating Power BI with Azure Synapse enables mid-sized banks to improve efficiency, risk management, and long-term competitiveness.