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 .…