The Challenge

The lender's fraud detection relied on a set of static rules hard-coded in their loan origination system. Adding a new fraud signal—a new device fingerprinting check, a velocity limit, a geographic anomaly flag—required raising an engineering ticket, going through a development and testing cycle, and waiting for a release window. During that window, the lender was exposed. Fraud patterns were evolving faster than the team could push code. There was also no execution logging, so when a fraud case was disputed, the risk team had no record of which signals had fired on that application.

What We Built

We built a real-time fraud detection rule engine with a configurable signal library covering identity anomalies, device fingerprinting, application velocity checks, bureau anomaly flags, and geographic signals. The risk team authors, tests, and deploys fraud rules through a no-code interface—without raising a single engineering ticket. Each rule is tested against a library of historical fraud cases before it reaches production. Every execution is logged: which signals fired, the individual scores, the combined fraud score, and the final decision.

Key Capabilities Delivered

• Configurable fraud signal library: identity, velocity, device, bureau, and geographic checks
• Real-time fraud scoring with sub-100ms response time
• No-code rule authoring for the risk team—no engineering dependency
• Sandbox testing against historical fraud cases before any rule is deployed
• Complete execution log: every signal, score, and decision path per application
• One-click rollback to any prior rule state

The Outcome

The risk team can now respond to emerging fraud patterns in hours rather than waiting for a development sprint. New signals are tested against the historical fraud case library before deployment, reducing false positive risk. The complete execution log gives the team clear evidence for dispute resolution and provides the structured documentation regulators expect during fraud audit reviews.