FinTech 5 months

Fraud Detection AI for PayShield

Deployed ML fraud detection that reduced chargebacks by 78% while cutting false positive rate to under 0.2% for a payment processor handling $2B annually.

Machine Learning AI Automation Custom Software Development

Verified Results NDA Signed US-Based PM Fixed Price

-78%

Chargeback Reduction

0.2%

False Positive Rate

99.2%

Detection Accuracy

<20ms

Inference Speed

-78%

Chargeback Reduction

0.2%

False Positive Rate

99.2%

Detection Accuracy

<20ms

Inference Speed

The Challenge

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.

Our Solution

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 Approach

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.

TechVerse's fraud ML reduced our chargebacks by 78% and simultaneously cut false positives from 12% to 0.2%. We're saving $12M annually. The ROI is extraordinary.

Nathan Ford

CTO, PayShield