Real-Time Fraud Detection Engine for High-Velocity Transactions

The Challenge

A global digital payments provider processing over $12B in annual transaction volume faced rising fraud rates from automated credential stuffing, card testing attacks, and synthetic identity fraud. Their legacy batch risk system evaluated transactions 15 minutes post-authorization, allowing compromised accounts to execute hundreds of fraudulent purchases before automated freezes took effect.

False-positive declines were costing the business $4.2M annually in lost revenue, while chargeback rates pushed the platform near Visa and Mastercard high-risk threshold limits.

Our Approach

We architected an inline, real-time fraud scoring pipeline that evaluates every payment authorization request in under 50 milliseconds before authorization approval.

Event Streaming & Feature Store: Transaction metadata, device telemetry, IP velocity, and behavioral signals are ingested via Apache Kafka into a low-latency feature store powered by Redis Cluster. Features such as 5-minute transaction velocity and geographical displacement are updated in real time.

Machine Learning Inference: We deployed an ensemble model combining XGBoost for structured feature scoring and an ONNX-runtime Transformer model for sequence pattern recognition. Models execute in C++ sidecar containers alongside payment gateway proxy nodes.

Dynamic Risk Action Engine: Instead of binary approval/rejection, the system applies dynamic risk actions — frictionless approval for low-risk transactions, 3D Secure (3DS) step-up authentication for medium-risk, and immediate block with automated account hold for high-risk attacks.

Results