Reducing Transaction Monitoring Alerts

Our CEO, Uri Rivner, was quoted in an insightful article by Rachel Wolcott from Thompson Reuters Regulatory Intelligence. The article sheds light on the importance of re-engineering anti-money laundering (AML) systems and controls, with a focus on know-your-customer (KYC) processes. By investing in KYC and modeling “good” customer transactions, firms can significantly reduce the overwhelming pile-up of transaction monitoring alerts.

According to the article, the shift towards digital onboarding has prioritized speed over collecting sufficient information to assess transaction suspicions. As a result, AML analysts are faced with the daunting task of sifting through thousands of alerts, searching for the proverbial needle in a haystack. However, Uri emphasizes the need for a proactive approach, leveraging technology such as artificial intelligence to understand customer behavior and differentiate between legitimate transactions and actual money laundering.

One of the key issues highlighted in the article is the reactive nature of transaction monitoring, where controls are triggered only when necessary. By conducting more thorough KYC checks during the onboarding process, firms can save time and money in the long run, avoiding the need to hire hundreds of people to review false positive alerts. The article also underscores the importance of establishing a minimum standard for KYC checks, particularly for low-risk customers, while also applying principles for high-risk customers.

Uri further emphasizes the friction and inconsistency in resolving transaction monitoring alerts through traditional methods such as phone calls. He suggests that digital contact with customers proves to be more effective, with a significantly higher completion rate and quicker resolution of queries. This highlights the need for continuous KYC processes and maintaining a dynamic understanding of customers’ financial behavior to minimize friction in the relationship.

To address the challenge of transaction monitoring, Uri advocates for modeling both good and bad transactional behavior. By leveraging AI and machine learning tools, firms can collect and analyze demographic and transaction data to differentiate between money laundering activities and legitimate customer transactions. This approach allows for the identification of genuine anomalies and the green flagging of transactions, ultimately improving efficiency and reducing false positives.

At Refine, we are committed to staying at the forefront of innovation in the fight against financial crime and ensuring a seamless experience for our valued customers.

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