AI-powered transaction monitoring can replace the manually defined, rules-based approach and harness the power of financial institutions’ own data to train advanced machine learning (ML) models to provide a comprehensive view of risk scores.

Tapping into a holistic view of your data, the model directs you to the highest weighted money laundering risks by examining transaction, account, customer relationship, company, and other data to identify patterns, instances, groups, anomalies, and networks for retail and commercial banks.

Each score provides a breakdown of key risk indicators, enabling business users to easily explain risk scores, expedite the investigation workflow, and facilitate reporting across risk typologies.