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. This allows the system to scan millions of "Risk_Score" values without loading the entire dataset into memory.
Visualization as a Diagnostic Tool
In fraud investigation, clarity is king. I engineered a high-contrast Investigation Deck that uses color-mapping to highlight anomalies.
Originally published at dattasable.com
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