Near-Prime Lending: The Role of Alternative Data and AI

This report will examine the characteristics of the segment of subprime borrowers classified as “near-prime” and why they struggle to obtain affordable credit. We will also explore new technologies and alternative approaches to assessing creditworthiness that may hold the key to more competitive loan pricing, allowing traditional lenders to win more business from consumers in the gray space between prime and subprime.

The timing of these developments is crucial, as wages are finally starting to grow amid persistently low unemployment. That means many consumers with blemishes on their credit profiles are increasingly in a position to safely take on additional debt. The challenge—and opportunity—for lenders is to differentiate consumers who exhibit persistently risky behavior from those who are moving past temporary setbacks in their financial life.

Key questions discussed in this report:

• What is a near-prime consumer?
• What are their habits? How do they compare with prime and subprime cohorts?
• What is the lending opportunity for near-prime consumers?
• What types of alternative data provide meaningful insights to lenders?
•  How can machine learning help lenders safely and efficiently underwrite loans for near-prime consumers?