Reduced Lending Risk and Better Repayment Rates With Enhanced Default Risk Models

Cut down on default rates by redefining small business lending scores with better data
Online lending has boomed in the years since the end of the great recession as governments loosened regulations and the lending landscape became friendlier and more tech-savvy. However, the increased competition means that lenders must make decisions much faster based on information that isn’t always truly indicative of borrowers’ true repayment potential.
The problem

An online small business lender has been looking to expand their operations but has seen a concerning rise in the number of defaults on the loans they have already extended. Their risk models are built around traditional financial records — bank statements and basic accounting documentation — but don’t take into account external factors. They have seen their default rate climb from the industry average 20% to nearly 30%, a worrying trend that has caused them to lose significantly month-over-month.

The solution

Our customer understood that the only way to remain competitive and still extend a good number of loans was to find a new way to measure their borrowers’ default risk. The company connected its internal data to Explorium to enrich it with alternative financial data that could provide greater visibility into borrowers’ financial health and activity. By enriching their data with thousands of external sources, the lender was able to generate the following features: 

  • Economic stability score based on previous online activity and purchases
  • Company data that includes years in operation, company type, and even search trends
  • Economic risk relative to geographic and region data
  • Average property tax and value of home region
  • Income and financial stability indicators in the area
The results:

Reduced lending risk and better repayment rates

Once their risk models were re-trained using the new datasets, the company managed to reduce their default rate from its high of nearly 30% to 21%. More importantly, they have been able to better vet potential borrowers and extend much safer loans that are more likely to be repaid. The result is a revenue boost of nearly 4% in the six months following the adoption of their new risk model alone.

Reduce lending risk using models enriched with external data.
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