Anti-Money Laundering

AI-powered AML systems detect suspicious transactions and improve compliance efficiency.

Business impact

• Detection accuracy — AI improves identification of suspicious transactions, reducing false negatives
• Compliance rate — Automated monitoring ensures adherence to AML regulations and reduces violations
• Operational efficiency — Automation reduces manual workload and speeds up investigations
• Investigation turnaround time — Faster analysis and alerts shorten time to resolve suspicious cases
• False positive rate — Advanced AI reduces unnecessary alerts, improving analyst productivity

Data requirements

• Transaction records (Structured) — Used to detect unusual patterns and suspicious activities
• Customer KYC data (Structured) — Provides identity and risk profiles for compliance checks
• Payment messages (e.g. ISO20022) (Text) — Enables real-time transaction monitoring and risk assessment
• Blockchain transaction data (Structured) — Analyzes crypto transactions for illicit activity detection
• External watchlists and sanctions data (Structured) — Supports screening against known high-risk entities
• IoT device data (Numeric) — Enhances behavioral profiling and identity verification

AI methods and techniques

• Predictive AI — Forecasts suspicious behavior and flags high-risk transactions
• Generative AI — Generates explainable insights and supports decision-making
• Agentic AI — Automates transaction monitoring and compliance workflows

AI models and model families

GPT-4o, Claude, Llama, Custom ML models, Graph neural networks

Industries

Real-world evidence

4 documented case studies on record.

Companies using this: Capgemini SE, EY Cayman Ltd, Hong Kong Virtual Asset Exchange HKVAX, Temenos.