Fraud Detection Lab
How AI/ML Architecture Stops Credit Card Fraud in Real Time
Rule-based fraud systems were designed for a world where fraud patterns were predictable and slow-moving. Modern fraud — card-not-present attacks, account takeovers, AI-assisted fraud rings — evolves faster than any rule set can be maintained. Static velocity rules catch the obvious, but miss the 40% of fraud that looks individually normal yet forms part of a coordinated attack.
This lab walks through a production-grade 8-layer detection pipeline — Kafka ingestion → real-time feature engineering → feature store → rule engine → 5 parallel ML models (LightGBM, LSTM, GNN, Isolation Forest, Geo) → weighted ensemble → decision engine — on 5 realistic fraud scenarios. Every decision is made in under 66ms. No backend, no database. Pure architecture showcase.
Detection Pipeline — 8 Layers
Click Run Simulation to trace a transaction through all 8 layers
Production Metrics
<66ms
End-to-end Latency
p99 decision time
94.7%
Detection Rate
true positive rate
0.31%
False Positive Rate
customer friction
$2.4M
Daily Fraud Prevented
avg across portfolio
97.2%
Ensemble Accuracy
AUC-ROC on holdout
99.99%
System Uptime
last 12 months
Interactive Detection Trace
Choose a Fraud Scenario
Card-Not-Present Fraud
Stolen card used online from Lagos, Nigeria at 2:47AM
Transaction Ingestion
Apache Kafka + Flink
- →Raw transaction event (JSON)
- →Card PAN + masked token
- →Device fingerprint header
- →IP + geolocation metadata
Signals Detected in This Scenario
Layer Narrative
Transaction arrives from TechZone Electronics checkout. Card token resolves to a Chicago-area customer. Kafka topic partitioned by card hash.
Impact Analysis
Why Pure Rules Fail — The ML Advantage
Rule-Only System
PARTIAL CATCHIP–BIN country mismatch rule would flag, but only to REVIEW queue (not auto-block). Velocity rules would not trigger (1 txn/hr is normal). Without ML, this would sit in a human review queue for 4–6 hours — long after the card could be used at other merchants.
Consequence
Delayed detection means the attacker has 4–6 hour window. Average CNP fraud ring tests 8–12 merchants after first success. Estimated loss: $3,200–$8,400 across multiple transactions before manual review triggers card suspension.
ML + Rules System
CAUGHTRule-only systems catch ~61% of CNP fraud. ML ensemble raises this to 94.7%. The 33% gap represents ~$890K monthly fraud loss avoided.
Value Delivered
Real-time ML ensemble detects patterns invisible to static rules: behavioral deviation, session sequences, graph topology, and geographic impossibilities — all within 66ms.