Money laundering presents a significant burden for banks. Criminals are constantly evolving their methods, making it increasingly difficult to detect and prevent fraudulent activities. Identifying the beneficial owner of criminal assets poses a particular challenge in many money laundering cases. Regulators have responded by introducing and rapidly changing legislation, placing a heavy burden on financial institutions. Moreover, the reliance on rule-based AML systems, often supplemented by hiring more employees, has limitations in effectiveness. The risk of high fines for non-compliance further compounds the challenges faced.  

To address the challenges of money laundering effectively, analysts can leverage AI models, combine data sets, and share workloads. These measures enable more accurate detection of potentially suspicious behaviour while reducing false alerts and operational costs. Utilizing smart risk detections helps mitigate AML risks, safeguard customer transactions, and maintain seamless customer journeys. A modular AML platform integrates internal and external data sources, employing rules and machine learning to accurately identify suspicious behaviour. It enables the monitoring of large transaction volumes in real-time or in batches, offering deeper insights, reducing false positives, and improving operational efficiencies.