AML/KYC AI Engine

Real-time financial crime detection with machine learning and graph analysis.

<5%

False Positives

Real-time

Detection

FATF/FinCEN

Compliant

The Problem

✗ Rule-based systems: 20%+ false positives
✗ $10B+ regulatory fines for failures
✗ Manual review: impossible at scale
✗ New schemes: rules can't adapt fast enough

Our Solution

✓ ML-based detection: <5% false positives
✓ Real-time transaction monitoring
✓ Fraud ring detection (graph neural networks)
✓ Continuous learning: adapts to new schemes

What's Included

KYC/CDD Automation

Identity verification, sanctions screening, PEP detection

Transaction Monitoring

Real-time detection of suspicious patterns and typologies

Fraud Ring Detection

Graph-based analysis to identify coordinated schemes

Regulatory Reporting

Automated SARs/CTRs with audit trails

Sanctions Screening

OFAC, UNSC, EU lists with continuous monitoring

Explainable Alerts

Why each alert triggered + evidence for investigators

How We Deploy

Cloud

AWS/Azure/GCP - scalable, managed

On-Premises

Full control, air-gapped compliance

Hybrid

Data on-prem, ML in cloud

Built For

Banks & Financial Institutions
Fintech & Payment Processors
Correspondent Banking Networks
Crypto & Digital Asset Exchanges
Money Transfer Services

Implementation Timeline

Week 1

Assessment

Understand your volumes, regulations, and systems

Week 2-3

Pilot

Deploy on subset of transactions, validate against your rules

Week 4+

Production

Full rollout with integration to your core systems

Technology

ML Models

Tree-based models like XGBoost, LightGBM, Graph Neural Networks, Autoencoders, Bayesian Methods, Monte Carlo Simulations

Infrastructure

Real-time streaming (Kafka), FastAPI backend, PostgreSQL/Neo4j

Ready to Reduce False Positives?

Let's talk about how AML/KYC AI can work for your institution.

Schedule a Demo →

Quick Questions

How is this different from our current rules engine?

ML adapts to new patterns automatically. Rules can't detect novel schemes. We typically reduce false positives from 50%+ to <5%.

Do you need historical transaction data?

Yes. 6-12 months of clean labeled data helps us train models faster. We can start with unlabeled data too.

What about regulatory approval?

Our approach complies with FATF, FinCEN, and EU AML directives. We provide documentation for supervisory exams.

Can you integrate with our core banking system?

Yes. We provide REST APIs and support batch/real-time integration patterns.

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