Real-Time AI Fraud Detection Pipeline
Build an AI-powered transaction monitoring system that detects fraud in real-time while reducing false positives by 50%.
Transformation
Before & After AI
What this workflow looks like before and after transformation
Before
Rule-based fraud detection generates thousands of alerts daily, with 95%+ being false positives. Investigators manually review each alert, creating massive backlogs. Sophisticated fraud patterns evade static rules. Average detection time is 48-72 hours after the fraudulent transaction.
After
ML models score every transaction in under 200 milliseconds. False positives drop by 50-70%, allowing investigators to focus on genuine threats. New fraud patterns are detected automatically through anomaly detection. Average detection time drops to real-time for known patterns and under 4 hours for novel schemes.
Implementation
Step-by-Step Guide
Follow these steps to implement this AI workflow
Map Fraud Landscape
2 weeksCatalogue known fraud types, current detection rules, and false positive rates. Analyse historical fraud cases to identify patterns that rules miss. Interview investigators to understand their decision-making process.
Prepare Training Data
4 weeksLabel historical transactions as fraudulent or legitimate. Address class imbalance (fraud is typically <0.1% of transactions) using SMOTE, undersampling, or cost-sensitive learning. Build feature engineering pipeline from transaction metadata, customer behaviour, and network patterns.
Build Real-Time ML Pipeline
8 weeksDevelop ensemble models combining gradient boosting for interpretable risk scoring with neural networks for complex pattern detection. Build real-time inference pipeline capable of scoring transactions within 200ms SLA. Implement streaming architecture (Kafka/Flink) for continuous processing.
Deploy With Human-in-the-Loop
4 weeksLaunch AI scoring alongside existing rules as a parallel system. Route high-confidence AI alerts directly to investigation queue. Build investigator dashboard showing AI risk factors, similar historical cases, and recommended actions. Gradually increase AI authority as confidence grows.
Continuous Learning Loop
OngoingFeed investigator decisions back into model training. Implement automated retraining pipeline triggered by performance drift. Build adversarial testing to simulate new fraud patterns. Report model performance metrics to compliance and regulators.
Tools Required
Expected Outcomes
Reduce false positive rate by 50-70%
Detect fraud in real-time (under 200ms for known patterns)
Increase fraud detection rate by 30-40% for previously undetected patterns
Reduce investigator caseload by 60%, allowing focus on high-value cases
Save $2-5M annually in prevented fraud losses (for mid-size bank)
Solutions
Related Pertama Partners Solutions
Services that can help you implement this workflow
Frequently Asked Questions
Typically within 2-3 months of deployment. The AI starts by matching rule-based performance while reducing false positives, then surpasses it as it learns from investigator feedback and detects patterns that static rules miss. We recommend running both systems in parallel during the transition.
This is why continuous learning and adversarial testing are built into the workflow. AI models are regularly retrained on new data, and we simulate adversarial scenarios to test model robustness. The advantage of ML over rules is that models can adapt to new patterns without manual rule updates.
Ready to Implement This Workflow?
Our team can help you go from guide to production — with hands-on implementation support.