• Optimized data capture and processing accuracy by 15% above benchmarks for over 10,000 customer activities. Automated data processes using Python, reducing retrieval and processing times by 30%, thereby optimizing workflows for the Chief Risk Officer.• Developed over 10 custom AWS QuickSight dashboards, visually presenting key risk, opportunity, and trend insights for segmented loan portfolios, empowering data-driven decision-making.• Analyzed and optimized SQL query plans to reduce execution costs and improve performance, achieving a 40% decrease in database load time for monthly reports.Created a customized tool for Under Writer• Integrated Gaussian Mixture Modeling and custom decisioning algorithms to develop a risk assessment tool for analyzing bureau data and 6-month bank statements, improving risk assessment accuracy by 20% and enhancing underwriting processes.• Improved comparative analysis of loan counts between bank data and bureau reports, identifying discrepancies and improving data consistency by 25%, thereby enhancing overall data reliability and accuracy.Segmentation of Customer• Utilized Decision tree modeling to identify four distinct customer segments. Enhanced credit policy development, increasing targeted marketing effectiveness by 30% and improving customer satisfaction.• Integrated empirical models with bank statement analysis to optimize resource allocation across four customer segments, achieving a 10% reduction in default rates.