Fraud Detection System — Global Fintech (UK)
Real-time transaction stream, risk scores, and investigator queue.
Client Overview
A rapidly scaling UK-based fintech providing merchant payments and BNPL services globally. As volumes increased and products diversified, the client faced rising fraud attempts, chargebacks, and account takeover (ATO) risks that required a robust, real-time detection capability.
Challenge
Existing rules were brittle and manual review backlogs were growing. Fraud patterns evolved quickly (synthetic IDs, mule accounts), demanding adaptive detection and efficient investigator workflows with explainable evidence to act fast without harming legitimate customers.
Solution — Adaptive Real-Time Fraud Platform
We built a layered fraud platform combining a streaming feature pipeline, ensemble ML models, a low-latency rule engine, and a human-in-the-loop investigation workspace with prioritized queues and auto-assembled evidence packs.
Core components
Approach
- Map fraud taxonomy and historical chargebacks to define target detectors.
- Build streaming features and test models in shadow mode to measure operational impact.
- Introduce risk-based routing: auto-decline high-confidence fraud; route medium-risk to investigators.
- Implement feedback loops: investigator actions feed model retraining and rule tuning.
Technology stack
Implementation — Key Steps
Phase 1 — Taxonomy & Data Pipeline (Weeks 1–6)
Consolidated historical fraud labels, instrumented device analytics, and set up streaming pipelines.
Phase 2 — Model Development & Shadow Mode (Weeks 7–14)
Trained ensemble detectors, ran shadow evaluations and measured precision/recall vs. legacy rules.
Phase 3 — Investigator Tooling & Automation (Weeks 15–24)
Built case UI, implemented auto-evidence packs, and deployed risk-based routing policies.
Phase 4 — Production Tuning (Weeks 25–32)
Tuned thresholds per product, reduced false positives, and formalized retraining pipelines.
Impact & Results
35%
Reduction in chargeback volume
50%
Decrease in investigator workload (by automation)
25%
Reduction in false positives after tuning
Weeks
Time to productionized models
Qualitative outcomes
- Investigators gained faster evidence collection which improved decision speed and consistency.
- Product teams could adjust risk-based policies per market with low operational friction.
- Real-time scoring reduced fraud losses while protecting customer experience.
Client Testimonial
Key Highlights & Learnings
- Shadow-mode validation is crucial to measure impact without business disruption.
- Auto-evidence packs dramatically speed investigator throughput and decision quality.
- Per-product thresholds enable balancing fraud loss vs. friction by customer segment.