Credit Unions and Regional Banks face great challenges from an AML standpoint for several reasons. Large banks have more funds available to maintain expensive compliance programs and for absorbing big fines when non-compliant. Smaller financial institutions need to be more and more efficient in identifying suspicious behaviors as the costs of maintaining AML compliance continues to grow. Furthermore, smaller financial institutions might be perceived as softer targets for cash-based activities like structuring deposits to avoid detection. Automated machine learning provides the ability to more efficiently monitor for suspicious activity.
Justin Dickerson, General Manager of Global Finance for DataRobot, and Dan Yelle, a Customer-Facing Data Scientist for DataRobot have decades of combined experience applying data science and machine learning to solve business problems in the FinTech, Insurance, and Banking industries. They work closely with partners in the Financial Services industry to ensure their machine learning initiatives are successful.
In this webinar, Justin and Dan show how automated machine learning can be used to reduce false positive rates, thereby 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