Machine Learning Creates Win-Win for Lenders and Consumers

TechCrunch’s Leena Rao reports on ZestFinance, a startup poised to transform the payday loan industry with a new underwriting model that yields more accurate assessments of a borrower’s creditworthiness.

The result? Greater access to credit for more people at more affordable rates and fewer defaults.

Founded by former Google CIO, Douglas Merrill, ZestFinance weaves together traditional credit scoring with big data analysis, machine learning and human expertise.

From the lender’s point of view, the value proposition is the ability to better:

  • Quantify a borrower’s likelihood to repay
  • Manage the risks of their loan portfolios

So far, the model outperforms current industry best practice with a 54% lower default rate amid twice the approval rate for loans.

The human element comes in the form of ZestFinance’s team of predictive modelers who are experts in mathematics, computer science and physics.

As machine learning algorithms uncover thousands of variables, the team looks at them in the context of patterns and trends they are seeing before releasing them into multiple big data models that run in parallel.

In the process, load decisions are returned in minutes and ZestFinance continually improves its algorithms.

For more, see the article here.

 

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