Determine the Value of Alternative Data and Machine Learning for Your Credit Business

It can be daunting to determine what data sources are worth evaluating in lending as more and more data is available than ever before.

Lenders often turn to credit data providers to reach new market segments, for example, borrowers with thin or no credit bureau files. However, these applicants have historically been very hard to underwrite.

To help, Jay Budzik, Chief Technology Officer at ZestFinance, provides the top three tips to help you identify the data sources that have the highest potential to help you reach new customers and market segments.

This white paper covers:
  • 3 tips to help you identify the data sources with the best potential for expanded customer reach
  • A case study of a large auto lender who used Zest software to develop a machine learning credit model that reduced losses over 20%, saving this auto lender tens of millions of dollars annually.

Fill out the form to download "3 Tips to Consider Before Using Alternative Data in Underwriting"