Our research explores how replacing a legacy rule-based fraud system with an ML platform could detect complex fraud patterns in under 35 milliseconds — projecting $15M in annual fraud prevention for a large financial institution.
Scenario: Large retail bank · Industry: Financial Services · Est. Timeline: 18 weeks
Many large banks are fighting a losing battle with their fraud operations. A legacy detection system — built on 200+ manually maintained rules — cannot adapt to evolving fraud patterns fast enough, and the operational fallout compounds over time.
The existing system relied on 200+ manually maintained rules that required weeks to update. Modern fraud rings exploited the gaps between rule updates, and the system missed 1 in 3 sophisticated multi-step attacks entirely.
A 12% false positive rate was blocking legitimate customer transactions and generating 40,000+ manual review cases per month. The operational overhead cost $3.2M annually and damaged customer trust through unnecessary card declines.
Any fraud scoring solution needed to operate within a strict 100ms p99 latency budget to avoid impacting the transaction approval flow processing 50M+ events per day across card, wire, and ACH channels.
The proposed solution is a streaming inference architecture that computes 180+ behavioural features per transaction in real time, scores each event with an ensemble ML model, and routes alerts to case management — all within 35ms.
A real-time feature store using Apache Kafka + Redis computes 180+ behavioural and transactional features per event within 5ms of ingestion — including velocity counters, merchant category patterns, and device fingerprint signals.
An ensemble of XGBoost + LightGBM models is trained on 3 years of labelled transaction data (2.1B+ events), with separate specialised models for card-not-present, ATM withdrawal, and wire transfer fraud — each tuned to the specific patterns of its channel.
Model serving is deployed on AWS Lambda with auto-scaling compute and a dedicated caching layer for high-frequency counterparties. Each transaction is scored in under 35ms end-to-end, well within a standard 100ms decisioning budget.
A priority-weighted alert queue feeds into the bank's existing case management system, with SHAP-based explainability scores for each flagged transaction. Fraud analysts see exactly which features triggered the alert — projected to reduce investigation time by 45%.
Estimated impact based on transaction volume modeling, ensemble ML benchmarks, and comparable fraud detection deployments in retail banking.
Projected detection rate versus the ~67% typical of legacy rule-based systems. The ML platform is designed to catch complex multi-step schemes that manual rule sets miss entirely.
Estimated annual fraud prevention based on transaction volume modeling — intercepting fraudulent card transactions and wire transfers that would clear under a legacy rule-based system.
Projected 82% reduction in the manual review queue, saving an estimated $2.6M in operational costs annually and dramatically improving the experience of legitimate customers whose transactions were previously being blocked.
Every engagement starts with a thorough understanding of your data, workflows, and objectives. Let's talk about what's possible for your organisation.