In the Online Lending community, we spend a lot of time discussing the quality of loans, including credit history, repayment behavior, interest rates, default rates, and ROI. Less often explored are the characteristics of a much larger population, the applicants who are declined by the origination platforms. What if LendingClub is just not that into you?
LendingClub has long maintained that they are able to achieve a high quality of loans and solid returns for lenders by maintaining stringent underwriting criteria and accepting only approximately 10% of applicants. Fortunately for us, LendingClub publishes data on rejected loan applications, which allows us to examine how this population differs from those who are approved.
In previous blog posts, we have explored the nature of FICO and credit scores in general. Now, let’s take a look at how declined LendingClub applicants stack up against those who are ultimately approved for a loan.
As we can see in the chart below, LendingClub is declining a massive population of low-FICO applicants. We also see that nearly 75,000 applicants had a missing FICO, which means that the TransUnion credit bureau was either unable to match the applicant to a verified consumer record or that the bureau did not have enough data to calculate a FICO score.
Clearly, there is a very significant appetite for credit in the marketplace, particularly among those with missing or low FICO scores.
Aside from following general state-level restrictions on P2P lending, the geographic distribution of declines does not differ greatly from that of LendingClub approvals, as detailed in an earlier post.
The early Online Lending exchanges have been able to grow and earn the confidence of investors by maintaining consistently high credit quality among the loans allowed on their platforms. This strategy has clearly paid off, but questions do remain. The fact that the declined population is so vastly larger than the approved is cause for discussion, and it is an open question as to whether or not it would be possible to somehow underwrite these loans in a way that they are priced effectively for their risk.
At a time when investors are itching for more borrower volume, there is likely significant capital out there willing to invest in a riskier but higher-yielding block of loans. However, doing so would require a greater level of predictive analytics to parse the good risks from the bad as well as a very finely tuned system for investment execution and portfolio management.