Case Study: Global Financial Institution achieves identification of $70M+ in high-risk loans and detection of synthetic identity fraud with Fraud.net’s machine learning models

Global Financial Institution Uses Fraud.net’s Machine Learning Models to Prevent Loan Fraud

Global Financial Institution was facing a rise in loan fraud—particularly first-payment defaults driven by complex synthetic identity schemes—that its legacy anti-fraud tools could not detect. To address this, the institution engaged Fraud.net and its machine learning models, leveraging Fraud.net’s Global Anti‑Fraud Intelligence Network for a targeted pilot to uncover hidden and emerging fraud patterns.

Fraud.net merged the bank’s seed sample of nearly 7 million loans with 5 million known identities from its intelligence network and applied its ML models in the pilot. The effort uncovered $70M+ in high‑risk loans (primarily synthetic identity fraud), flagged over 170,000 additional potentially risky accounts, and—after pilot success—led to Fraud.net’s model being deployed in production to augment the institution’s existing fraud prevention systems.