Abstract
This study analyzes more than 28 million recent loan listings on LendingClub, one of the world’s largest online marketplace lending platform. Using tree-based machine learning, we develop robust predictive representations of funding decisions on this fintech peer-to-peer lending platform. We find that a borrower's employment length is the main factor in the preference of lenders making funding decisions. The significant role of employment length is consistent with the widespread use of the lending platform to obtain better refinance for existing obligations. Requested amount and the existing leverage of a borrower are secondary in lenders' consideration. The credit pricing charged on a funded listing fully depends on the loan grade assigned by LendingClub. Monetary policy seems to have little impact on funding decisions on this platform.
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