Important factors determining Fintech loan default: Evidence from a lendingclub consumer platform
Tarunsai Korivi and
Journal of Economic Behavior & Organization, 2020, vol. 173, issue C, 270-296
This study examines the default determinants of Fintech loans, utilizing a sample of more than a million of personal loans that were originated through the LendingClub consumer platform during the period 2007–2018. We identify a robust set of contractual loan characteristics, borrower characteristics, and macroeconomic variables that are important in determining the likelihood of default, such as loan maturity, homeownership, loan purposes, occupation, etc. We also find an important role of alternative data in determining the default, even after controlling for the obvious risk characteristics of the borrowers, loan characteristics, and the local economic factors. The results are robust to different empirical approaches. Results imply that it would be important for regulators to provide greater transparency in terms of guidance and regulatory clarity on which alternative data can be used legally without violating fair lending rules. Lenders need to pay closer attention to how they make decisions and understand their own decisions that may be driven by complex algorithms inside the “black boxes.”
Keywords: Big data; Crowdfunding; Financial innovation; Household finance; Lasso selection methods; Machine learning; Peer to peer lending; P2P/marketplace lending (search for similar items in EconPapers)
JEL-codes: D10 D14 G20 G21 G (search for similar items in EconPapers)
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Working Paper: Important Factors Determining Fintech Loan Default: Evidence from the LendingClub Consumer Platform (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jeborg:v:173:y:2020:i:c:p:270-296
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