The Anti Money Laundering AI (AML AI) service has already been tested by HSBC to monitor for suspicious activity.
Money laundering is a serious criminal offense in most jurisdictions around the world. It is commonly associated with illegal activities, including drug trafficking, corruption, fraud, organized crime, and terrorism financing.
Governments and financial institutions employ various measures, such as anti-money laundering (AML) laws, regulations, and monitoring systems, to detect and prevent money laundering activities.
To help tackle the money laundering problem Google has placed a big bet on machine learning tools. The tech giant says its AML AI service examines billions of records and transactions for trends and signs of financial crime.
Google says this minimizes wasted investigators’ time as it reduces the number of alerts and provides explainable outputs to speed up investigations.
Jennifer Calvery, group head of financial crime risk and compliance at HSBC said “Google Cloud’s AML AI has significantly improved HSBC’s AML detection capability.
Google’s models already demonstrate the tremendous potential of machine learning to transform anti-financial crime efforts in the industry at large”.
Benefits of Google Cloud’s Anti-money laundering tool:
Increased Risk Detection:
It detects nearly 2-4 times more confirmed suspicious activity, strengthening your anti-money laundering program.
Lower Operational Costs:
It eliminates over 60% of false positives and focuses investigation time on high-risk, actionable alerts.
Robust Governance and Defensibility:
It gains auditable and explainable outputs to support regulatory compliance and internal risk management.
AI-powered transaction monitoring replaces the manually defined, rules-based approach and harnesses the power of financial institutions’ 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 money laundering risks by examining transaction, account, customer relationship, company, and know-you customer 9KYC) data to identify patterns, instances, groups, anomalies, and networks for retail and commercial banks.
Each score provides a breakdown of critical indicators, enabling business users to easily explain risk scores, expedite the investigation workflow, and facilitate reporting across risk typologies.