Industry: Fintech / Financial Services
Overview
This case study details the deployment of an AI-driven risk modeling and real-time fraud prevention engine for a private equity-backed digital lending platform. By applying machine learning to multi-source financial data, the system flags fraudulent applications and calculates borrower risk profiles instantly.
Business Problem
The lending platform was constrained by high risk metrics:
- Traditional, static credit scoring locked out credit-worthy borrowers with thin files, limiting user growth.
- The platform was targeted by sophisticated loan application fraud, causing financial write-offs.
- Manual underwriting reviews created transaction bottlenecks, causing customer drop-off.
How MyntiQ Tech Helped
MyntiQ Tech built a smart underwriting and security framework:
- Advanced Risk Assessment: Trained machine learning models on alternative data sources, including transactional cash-flows and utility histories, to assess borrower risk accurately.
- Behavioral Fraud Detection: Integrated device fingerprinting, behavioral biometrics, and IP verification to flag automated bots and stolen identities.
- Real-Time Decisions: Built an API engine that processes application queries in milliseconds, approving low-risk users instantly.
Business Outcomes Delivered
- Reduced loan default rates significantly, protecting capital assets.
- Increased applicant approval rates without raising overall risk exposure.
- Identified and blocked fraudulent loan applications, saving millions in potential write-offs.
- Automated over 85% of decisions, speeding up customer access to credit.
