Finance AI Project – Real‑Time Fraud Detection & Risk Assessment

Brief and project description

Deployment of AI systems in a retail bank to detect payment fraud and enhance risk assessment across credit, market, and operational risk portfolios, replacing largely rule‑based monitoring.

Quantified/Documented impact

Higher fraud detection accuracy and lower false positives vs rules alone, translating into reduced fraud losses and smoother customer experience.
Faster and more granular risk assessment, improving decision speed and operational efficiency in credit and market risk teams.

Strategy

Implement supervised/unsupervised ML and deep learning on transaction streams to flag anomalous behavior in real time
Integrate AI credit and risk models alongside traditional scoring to refine loan, limit, and capital decisions

Design

Production integration in core banking systems: real‑time scoring of card, wire, and digital payments plus periodic portfolio‑risk runs.
Evaluation via fraud loss reduction, false‑positive rate, approval rates, and portfolio default metrics.

Client

Large retail/consumer bank seeking to cut fraud losses and improve risk‑based pricing while staying compliant with GDPR/CCPA and banking regulations.

Project Info