Sentinel: AI Fraud Detection & Sanctions Screening

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The results

88% fraud detection accuracy
Catch more fraud while reducing false positives

30ms API latency
Real-time scoring that doesn't slow down checkout

97% sanctions precision
Screen against OFAC lists with confidence

$225K demonstrated savings
Optimize decision thresholds for maximum ROI

The problem

Payment companies face impossible tradeoffs:

Fraud is rising: Card-not-present fraud costs billions annually

False positives kill conversion: Legacy systems flag 10-20% of good customers

Compliance is complex: OFAC screening must happen in milliseconds

Traditional rule-based systems can't keep up with evolving fraud patterns.

The solution

Sentinel combines ML-based fraud detection with real-time sanctions screening:

Fraud detection

LightGBM gradient boosting trained on 429 features
Explainable AI (SHAP values) for regulatory compliance
Calibrated probabilities with cost-optimized thresholds

Sanctions screening

Fuzzy name matching against 39,350 OFAC entities
Handles typos, name variations, and transliterations
Sub-50ms screening with 97.5% precision

Production-ready API

FastAPI with async processing
Redis caching for sub-200ms latency
PostgreSQL audit logs for compliance

How it works

Submit a transaction via REST API or demo UI
Get instant results: fraud risk score, SHAP explanations, sanctions match
Make informed decisions with transparent, auditable predictions

Built with LightGBM, FastAPI, Redis, and Next.js.

Try live demo

Research project disclaimer

WARNING

This is a research case study demonstrating production-grade ML engineering. Models are trained on public datasets (IEEE-CIS, PaySim) for non-commercial research use only.

For production deployments: We train custom models on your transaction data, achieving 20-30% higher accuracy by learning your specific fraud patterns.

Pilot Sentinel with your transaction data

We're offering free pilots to Series A-C payments companies.

What you get

Custom fraud detection model trained on your transaction data
2-3 week side-by-side comparison with your current system
Detailed analysis: accuracy improvements, false positive reduction, cost savings

What we need

Access to 3-6 months of historical transaction data (anonymized customer data accepted)
Weekly check-ins to review results and gather feedback
A testimonial if we reduce false positives by 20%+ or improve fraud detection by 15%+

No cost. No long-term commitment. If we don't hit the improvement threshold, you've lost nothing. If we do, you get early access to production-ready fraud detection built for your specific patterns.

Spots available: 5 companies per month

Book pilot kickoff call

Try it yourself

Try live demo — Test with sample transactions

View source code — Apache 2.0 licensed

About Devbrew

We build custom AI solutions for cross-border payments companies. Sentinel demonstrates our technical depth in ML engineering, real-time systems, and regulatory compliance.

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Questions? Contact us | View repo: GitHub | LinkedIn