In the financial services sector (BFSI), fraud detection isn't just a feature - it's the primary line of defense. When dealing with 10,000,000+ transactions , a system must be more than fast; it must be surgically precise. The Challenge: Identifying Needles in a 10M-Record Haystack Traditional threshold-based systems often fail at scale because they generate too many "False Positives." For the BFSI Sentinel project, I focused on building a multi-dimensional risk-scoring engine that evaluates transactions across several vectors simultaneously. The Sentinel Core: Technical Milestones 1. Advanced Risk Scoring (ARS) Instead of simple "If-Then" logic, the Sentinel evaluates transactions using a weighted Risk Score. By correlating Transaction Amount , Temporal Velocity , and Regional Risk Deltas , the system assigns a high-fidelity score. 2. Performance Benchmarking with DuckDB To ensure sub-second response times on 10M rows, the Sentinel utilizes a Columnar Storage Engine .…