Fraud Detection
Systems

We analyze historical transaction records and common fraud patterns in your industry to define primary risk indicators.

We establish secure, low-latency data pipelines using Kafka to stream transaction and user event logs into the ML model.

We train anomaly detection and classification models using XGBoost, PyTorch, and historical dataset logs.

We integrate the ML scoring model with a dynamic rule engine, allowing your team to block or flag specific patterns manually.

We design a secure back-office case management portal featuring detailed risk scoring details and explanation layers.

We deploy the system to production with continuous shadow testing and set up auto-retraining pipelines to adapt to new fraud tactics.