Sanction Screening

AI automates sanction screening to improve compliance and reduce manual effort

Business impact

• Investigation time — AI automation reduces time analysts spend on each sanction screening case
• Operational efficiency — Streamlined workflows and orchestration lower manual processing overhead
• Compliance accuracy — Smarter algorithms reduce false positives and improve detection of true risks
• Analyst productivity — Analysts focus on high-value investigations instead of repetitive manual checks

Data requirements

• Sanctions watchlists (Structured) — Used to match customers and transactions against government-imposed restrictions
• Customer transaction data (Structured) — Provides context for risk assessment and anomaly detection
• Customer identity information (Text) — Supports identity verification and name matching against sanction lists
• Historical investigation records (Text) — Used to train models to reduce false positives and improve screening accuracy

AI methods and techniques

• Predictive AI — Predicts likelihood of true matches and flags suspicious transactions
• Agentic AI — Orchestrates workflows and automates escalation and investigation processes
• Symbolic AI — Applies rule-based logic for compliance with regulatory screening requirements

AI models and model families

GPT-4, Claude, Llama 2, Custom ML models

Industries

Real-world evidence

2 documented case studies on record.

Companies using this: Behavox, Rabobank.