Enterprise fraud-detection programme — streaming data pipeline, real-time ML scoring engine, rule-plus-model hybrid approach, and case-management integration with the SOC. Model-risk governance framework passing regulator review on first submission.
The bank had operated a rule-based fraud system for a decade — effective against known patterns but brittle against evolving attack types and generating a very high false-positive rate (80%+ of alerts were not fraud). The fraud team was overwhelmed by volume and increasingly missing novel attacks.
The challenge was four-fold. First, model explainability — any production model had to be auditable and defensible to the regulator. Second, latency — card authorisation required sub-200ms decisioning. Third, the rule engine had to remain — replacing it would have created too much operational risk. Fourth, the governance framework had to pass regulator review on first submission.
We designed a hybrid architecture: the existing rule engine continued to operate as a first-line defence; the ML model ran in parallel and returned a risk score; decisioning logic combined the two into a final action. This allowed the ML model to be introduced gradually — first in shadow mode, then for non-declining actions (enhanced monitoring), then for declining decisions — with the rule engine continuing as the primary safeguard.
Model development followed strict MLOps discipline: feature store, model registry, shadow deployment, A/B testing, drift monitoring. The governance framework — documented in the format regulators expect — covered model development, validation, deployment, ongoing monitoring, and retirement.
"What set this programme apart was the governance foundation. Most fraud projects we've seen produce good models with poor documentation. This one gave us a framework we can maintain and defend for years."
— Chief Risk Officer, bank · [VERIFY]Month 1–3: data pipeline build, feature engineering, initial model training. Month 4–6: shadow deployment, governance framework documentation, regulator engagement. Month 7–8: enhanced-monitoring deployment (non-declining actions). Month 9–10: full deployment including declining decisions, case-management integration with SOC.
Fraud losses reduced 44% year-over-year against a benchmarked baseline. False-positive alerts dropped 28%, meaningfully reducing fraud-team workload. Transaction scoring latency stayed under 200ms at the 99th percentile. The governance framework was accepted by the regulator on first submission — unusual in this space.
Apache Kafka for streaming, Microsoft Azure ML for model training and deployment, custom feature store, integration to card-management system, case-management workflow in existing SOC platform [VERIFY], model-risk documentation in regulator-specified format.
90 minutes, no slides. A senior partner, your relevant executive, and a whiteboard. We frame the opportunity, the risk envelope, and the engagement shape — then agree whether there's a fit before any proposal is drafted.