WorkFusion, a leading provider of AI digital workforce solutions for banking and financial services, announced that it has rolled out its newest Digital Worker, Isaac, an AI Transaction Monitoring Investigator. Isaac assists with transaction monitoring (TM) alert management by using machine learning capabilities to work first-level alerts, auto-escalate alerts that are likely to require investigation, and auto-closes alerts that are non-suspicious with supporting narrative and documentation, allowing AML analysts to focus on the highest risk activity.
Transaction monitoring is required for anti-money laundering/countering the financing of terrorism (AML/CFT) programs globally and is a critical tool for fighting financial crime. However, it can be a difficult compliance obligation. Banks manually review millions of transaction monitoring alerts each month with most of those alerts being non-suspicious. Regardless, suspicious activity monitoring programs take a lot of time, require large teams of people, and cost a lot of money.
“Transaction monitoring is a time-consuming and expensive, yet critical component of AML compliance,” said Art Mueller, WorkFusion’s Vice President of Financial Crime. “Every day, teams of analysts within banks review large numbers of alerts associated with transactions, patterns or behaviors that flag as potentially suspicious for money laundering or other financial crime. Analysts must determine whether these alerts are false positives, which typically 90-95% are, or truly suspicious activity. Our new AI Digital Worker, Isaac, reduces the alert review burden by helping to identify which alerts need to be escalated for further review and auto-closes those that it deems as non-suspicious. Because Isaac creates an easy-to-read dossier with a supporting narrative and documentation, analysts move from authors of reports to editors – saving their time to work on higher-risk and higher value investigations.”
Isaac’s responsibilities as an AI Transaction Monitoring Investigator:
- Picks up alerts generated from surveillance monitoring systems and then investigates and evaluates the activity.
- Automates for L1 transaction monitoring alert reviews, collecting data and either closing non-suspicious alerts or escalating them to an investigator.
- Creates a dossier of each decision supported by human-readable justification, supporting documentation, and a confidence threshold to maintain transparency for examiners and auditors.
- Provides a consistent approach to alert review and investigation, as well as the quality of the alert narrative and supporting documentation.
- Assists with common AML transaction monitoring scenarios that generate a high volume of alerts such as structuring, excessive funds transfers/movement of funds/patterns of funds transfers, unexpected account usage/behavior, high-risk factors, use of dormant accounts, and more.
Isaac is not a transaction monitoring tool and does not create alerts, rather, he works with leading transaction monitoring systems to help financial institutions manage TM alerts.
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