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
Presenters
DataRobot is an indispensable partner helping us maintain our reputation both internally and externally by deploying, monitoring, and governing generative AI responsibly and effectively.
The generative AI space is changing quickly, and the flexibility, safety and security of DataRobot helps us stay on the cutting edge with a HIPAA-compliant environment we trust to uphold critical health data protection standards. We’re harnessing innovation for real-world applications, giving us the ability to transform patient care and improve operations and efficiency with confidence
DataRobot provides us with innovative ways to test new ideas. Given a problem and a dataset, DataRobot allows us to generate multiple prototypes 20% faster. And the process facilitates the learning evolution of our data scientists.
The value of having a single platform that pulls all the components together can’t be underestimated. Then there’s the combination of the technology and the collaborative DataRobot team. If either one of those wasn’t there, I would have looked elsewhere.