Practical approach to Machine Learning based debt collection strategy in product

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

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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.

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Nate Spiegel

VP, Process Improvement & Change Execution, Citizens Bank

Ashish Khandelwal

Director – Product Management, EdgeVerve