Strategy and Solutions

Zymr developed and deployed an AI-based fraud detection module that embedded advanced analytics directly into the client’s loan origination workflows. The system used historical borrower behavior analysis to identify risk-prone profiles, document pattern recognition to detect tampering or inconsistencies in pay stubs, tax returns, and IDs, and credit file anomaly detection to flag irregularities in applicant histories. The fraud detection model was continuously retrained on live data, allowing it to adapt to evolving fraud tactics. This integration ensured high detection accuracy without impacting the platform’s rapid pre-approval experience.

About the Client

A digital-first mortgage brokerage offering instant pre-approvals and competitive rates via an online platform. The client sought to detect fraudulent applications without slowing their sub-60-second approval promise.

Key Outcomes

Increased detection of synthetic identity fraud cases by 25%.

Enhanced LOS efficiency through seamless integration with fraud detection APIs.

Business Challenges

Fraudulent applications ranging from falsified income documents to synthetic identities were slipping through initial checks, leading to increased default risk. Manual fraud review processes were too slow for their rapid approval of the SLA. The broker needed an AI-driven fraud detection system that could integrate seamlessly with their existing LOS.

Business Impacts / Key Results Achieved

Fraudulent application volume dropped by 30%, while maintaining the broker’s sub-60-second pre-approval speed. False positive rates fell by 20%, reducing unnecessary manual reviews.