approach-debt-bgs
approach-debt-bgs

Practical approach to Machine Learning based debt collection strategy in product

Date: Jun 18, 2019
Time: 12 PM – 1 PM EDT America/Detroit
LOCATION: Webinar

Home > Events > Practical Approach To Machine Learning Based Debt Collection Strategy In Product

About The Webinar


In recent times, AI has earned reputation of a superhero that can solve business problems that cannot be addressed by traditional technologies. However, according to an MIT SLOAN and BCG report, on the ground only 1 in 20 companies have been able to extensively utilize AI in their business. The huge gap between the expectation and actual delivery is due to a wide variety of challenges and concerns that enterprises face before a model can be deployed into production.

In this webinar, Ashish Khandelwal from EdgeVerve, an Infosys product subsidiary, and Nate Spiegel from Citizens Bank share their thoughts on the need for a Machine Learning based debt collection strategy and practical solutions to problems around auditability of a Machine Learning models, time to value, complex skill requirements and continuous monitoring of value delivered after the model is successfully deployed.

Register for the Webinar

 

 

Speakers

Nate Spiegel

VP, Process Improvement & Change Execution, Citizens Bank

Ashish Khandelwal

Director – Product Management, EdgeVerve