Induj Gupta

Full-stack · AI · Security

0%

Fraud Risk Manager

(2026)

The problem
Turn fraud scores into decisions a reviewer can explain, within the number of cases a team can actually review.
What I built
A supervised XGBoost detector and an Isolation Forest verifier score each transaction. A capacity-aware policy blocks, allows, or routes disagreements to a person, with SHAP explanations on every decision.
The result
On an IEEE-CIS temporal holdout, the capacity-capped detector showed 40.6% lower modeled loss than an amount-threshold baseline. That is an offline evaluation with modeled costs, not production savings.
Applied ML / Fintech · Solo build · XGBoost · TreeSHAP · FastAPI · Next.js
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