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Automated Buying of Notes on Lending Club Folio Secondary Market

Loan Supply and Demand Information for Lending Club Retail Platform

What is your Loan Selection Strategy?

Posted by Anil Gupta | Tuesday June 21, 2016, 3:13 pm | Categories: Lending Club

Recently, I reached out to a few PeerCube users about their lending strategies on Lending Club primary and Folio secondary platforms. The ensuing discussion was very enlightening. This blog post is a summary of pros and cons of simple lending strategies and an attempt to broaden the discussion to wider readership.

On Lending Club primary platform, the retail lenders are using a wide variety of loan selection strategies. Some lenders prefer simple strategies such as selecting loans only based on credit grade and loan term while others use complex strategies such as those based on data mining and machine learning.

The following arguments are in favor of using simple lending strategies based exclusively on risk assessment by the platform:

  • Information asymmetry. Lending Club has more information about borrowers than made available to retail lenders on its platform. Thus, any credit model developed based on additional information is bound to be more robust.

  • Consumer credit knowledge. Most retail lenders don't have background in consumer credit as lender even though they do have some experience as borrowers through mortgages, student loans, and credit cards. The retail lenders are unlikely to be able to gage the credit quality of loans on their own.

  • Credit modeling and risk assessment. Lending Club has a team of experienced credit analysts to develop credit models and assess risks. Lending Club risk models are going to be much more robust than what retail lenders or third party service providers may come up with.

These arguments are valid reasons for retail lenders to defer risk assessment to Lending Club and only use credit grade provided by Lending Club as main criteria for loan selection. However, there are a few concerns with such a simple strategy.

Lending Club generates most of its revenue through loan originations. To grow revenues, Lending Club needs to attract and offer loans to expanding range of borrowers. One way to increase loan applicants is to relax credit quality making more applicants become eligible for loans. Any strategy based on Lending Club credit model will be susceptible to such changes in credit quality. Retail lenders can be much more selective in deciding which borrowers to lend to.

In June 2013, we discussed one such loosening of minimum credit criteria for borrowers in our blog post Impact of Changes in Lending Club Credit Criteria on Credit Grade - Six Months After. The Table 1 below shows a sample of changes made in minimum credit crietria for borrowers over the years. On the other hand, after the crisis in May, Lending Club tightened the minimum credit criteria for borrowers in order to present more attractive loans to lenders. Such business decision may enable Lending Club to retain and attract lenders who had slowed down or stopped funding of loans after the turmoil. As you can see, the risk assessment can change based on the business decisions of platform.

Table 1: Known changes in minimum credit criteria by Lending Club

Credit Attribute August 2012 November 2012 August 2014 June 2016
FICO Score > 660 > 660 > 660 > 660
Debt-to-Income (DTI) < 35% < 35% < 40% < 35%
Credit History > 36 months > 36 months > 36 months > 36 months
Inquiries in last 6 months < 3 for FICO < 740, < 8 for FICO > 740 < 6 < 5 < 5
Revolving Trade Accounts Open > 2 > 2 > 2 > 2
Current delinquencies 0 Any Any Any
Recent bankruptcy 0 Any Any Any
Tax liens 0 Any Any Any
Non-medical collection in last 12 months 0 Any Any Any
Revolving Credit Balance < $150,000 Any Any Any
Credit Utilization < 98% Any Any Any

A retail lender is more likely to participate in small number of loans compared to the originations volume in a year. For example, in 2015, Lending Club issued over 400,000 loans. If you lent to all loans in this pool of 400,000 loans, your portfolio performance will be same as the pool. However, if you lent to a small number of loans from this large pool, there will be large variability in performance of your portfolio compared to the pool of all loans. It is conceptually similar to investing in broad index based fund (large pool of stocks) versus investing in handful of stocks in your stock portfolio.

Statistically, selecting a small number of loans from a large pool of loans may result in big spread in portfolio performance across different retail lenders. A few unlucky lenders may have much lower portfolio returns than the average return for the large pool of loans. A loan selection strategy that reduces the number of acceptable loans in the pool may increase the chance of portfolio returns being closer to the expected average for the smaller pool and reduce the variability in portfolio performance. In future blog posts, we will attempt to quantify and explore this further.

What type of loan selection strategy do you prefer and why?

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