Regional and mid-size banks face challenges when implementing and optimizing their Bank Secrecy Act (BSA) and anti-money laundering (AML) programs. Unlike larger banks, regional banks have less funds for AML so they need to be more efficient in identifying suspicious behaviors as the costs of AML compliance continues to grow. Furthermore, smaller banks are perceived as softer targets for cash-based activities like structuring deposits to avoid detection.
Automated machine learning helps mitigate risk by more efficiently monitoring for suspicious activity. In this webinar, you’ll see how automated machine learning can be used to reduce false positive rates, improving the efficiency of AML transaction monitoring and reducing costs.
You'll discover how Automated Machine Learning provides:
- The ability to develop and refresh AML predictive models at any time
- The ability to deploy models with a click of a button
- The ability to operationalize AML models by following a process that is user-centric