Fraudulent transactions across credit/debit cards, internet banking, and mobile banking must be caught and stopped in milliseconds, not hours. To achieve this I am commissioning an AI-driven Fraud Risk Management platform that leans on predictive analytics as its core detection technique. The solution should stream incoming transactions, enrich them with historical behaviour, and score each event in real time, surfacing high-risk cases to my analyst team while allowing genuine customers to transact without friction. A lightweight rules layer may sit on top for regulatory transparency, but machine-learning models should own the heavy lifting and continually retrain from fresh data. I will provide direct access to transaction feeds and account activity logs along with any other internal data you advise is critical. External datasets can be added later, so the design must remain modular. Deliverables • A production-ready ML pipeline: data ingestion, feature engineering, model training, validation, champion–challenger, automated retraining • Real-time scoring service with REST or gRPC endpoints that can sustain peak banking traffic with sub-second latency • Analyst dashboard (web-based) showing alerts, model confidence, and investigation notes • Deployment scripts (Docker/Kubernetes preferred), monitoring hooks, and rollback strategy • Clear documentation and a handover session covering model logic, data schema, and operational playbooks Acceptance criteria • ≥95 % detection of known fraud patterns from a blind test set with ≤0.5 % false-positive rate • End-to-end latency below 500 ms for 99 % of transactions under simulated peak load • Audit trail satisfying local banking compliance requirements Python, Spark, Kafka, TensorFlow/PyTorch, Kubernetes, and Grafana are all acceptable; feel free to propose alternatives if they meet the same real-time guarantees. Continuous improvement is key, so code quality, unit tests, and CI/CD pipelines will be part of the review.