Building a better mousetrap – Peer to Peer Lending edition

Interesting paper over at the NBER about peer to peer lending. I highly recommend reading the entire paper.

The TLDR version:

  • A market set Interest rate is better predictor of default over credit score. This is primary because credit scores are backward looking.
  • For lower quality borrowers, soft/non standard information is relatively more important (in this case for outcome=Fraction repaid) over credit score (this was very counter intuitive  to me)
  • Social data (pictures/friend recommendations) did not make a difference either way (not empirically correlated to default). another very interesting result.
  • Banks do not look at non standard data especially for smaller loan sizes, not scalable for them

This paper had me thinking on the various implications for a product in this space.

  • p2p lending in the smaller loan sizes is a huge opportunity, Banks are not playing in this space.
  • Does social even matter?
  • Should credit score be a major signal for lower quality borrower in your model? What other soft information can you augment in your model to provide better signal?

We live in interesting times 🙂


Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s