Fraud Detection AI for PayShield

PayShield was processing $2B in annual payments with a fraud rate of 0.8% — costing $16M annually. Their rule-based fraud system generated a 12% false positive rate, blocking legitimate customers and creating massive churn. They needed ML without killing conversion.

For many FinTech organizations across the United States, this type of operational bottleneck is all too familiar. Manual processes, legacy systems, and disconnected workflows create compounding inefficiencies that cost both time and revenue — often without leadership having a clear line of sight into the true cost.

PayShield needed a partner who understood the technical complexity and the business urgency. Delivery speed mattered, but so did long-term maintainability, security, and the ability to scale as the business grew.

We built a real-time fraud detection system using gradient boosting and graph neural networks that analyze 150+ signals per transaction. The model runs inference in under 20ms and achieves 99.2% fraud detection with only 0.2% false positives.

Our engineering team architected the solution with production scalability in mind from day one — not as an afterthought. Every component was evaluated against real-world load expectations, and the system was designed to handle growth without requiring expensive re-architecture six months after launch.

We maintained weekly video demos with PayShield's leadership throughout the build. This meant no surprises at launch and full stakeholder alignment at every milestone. Every sprint delivered working, tested software — not just progress reports.

We used a two-stage approach: a fast gradient boosting model for initial scoring, with a slower graph neural network analyzing transaction relationships for high-risk cases. The system learns from confirmed fraud patterns weekly.

The FinTech sector in the United States is undergoing rapid digital transformation. Organizations that invest in custom software and AI-powered automation today are building structural advantages that will be extremely difficult for competitors to close — lower cost structures, faster response times, and better customer experiences compounding year over year.

This project for PayShield is a strong example of what's achievable when business requirements are clearly defined, technology choices are made deliberately, and delivery is structured around measurable outcomes rather than billable hours.

For US companies in the FinTech space evaluating similar investments: the ROI case is typically clearer than expected, and the risk is manageable with the right partner and the right contract structure. Fixed-price engagements with milestone-based payments and clear acceptance criteria protect both sides and keep projects on track.