Read the latest White Paper sponsored by EdgeVerve.

A More Intelligent Approach to Debt Collections

Using Machine Learning to Improve Profitability and Customer Satisfaction

White Paper • 2019

Sponsored by

What is this paper about?

A growing number of banks and lending innovators are discovering that machine learning, applied to debt collections, can dramatically grow recovery, reduce costs and improve customer retention. This paper explores the drivers to improve collections performance, leading-edge solutions, machine learning implementation best practices, and ROI potential. Findings are based on interviews with innovative lending institutions and solution providers. Sponsored by EdgeVerve.

Key Points:

  • Machine learning’s potential to improve collections touches: default prediction, borrower risk segmentation, personalized customer outreach, tailored settlement and recovery solutions, and agent skill enhancement.
  • Significant ROI is being realized in recovery rates, decreased collection costs, and customer engagement
  • Implementation time-to-value can be as short as weeks to months
  • There currently are only several robust commercial solutions
  • Leading solutions are clustering according to two designs - fully-outsourced collections processes, and easy-to-integrate "overlays" with existing in-house systems

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