Funding Decisions in Online Marketplace Lending

HKIMR Working Paper 01/2020

29 Pages Posted: 8 Jan 2020 Last revised: 5 Mar 2020

See all articles by Chris K.C. Ip

Chris K.C. Ip

Hong Kong Monetary Authority - Hong Kong Institute for Monetary Research (HKIMR)

FY Eric Lam

Hong Kong Monetary Authority - Hong Kong Institute for Monetary Research (HKIMR)

Date Written: January 9, 2020

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.

Keywords: Financial Technology; Fintech Lending; LendingClub; P2P Lending; Peer-to-peer Lending; Shadow Banking

JEL Classification: G21; G23

Suggested Citation

Ip, Chris K.C. and Lam, Full Yet Eric Campbell, Funding Decisions in Online Marketplace Lending (January 9, 2020). HKIMR Working Paper 01/2020. Available at SSRN: https://ssrn.com/abstract=3505057 or http://dx.doi.org/10.2139/ssrn.3505057

Chris K.C. Ip

Hong Kong Monetary Authority - Hong Kong Institute for Monetary Research (HKIMR) ( email )

3 Garden Road, 8th Floor
Hong Kong
China

Full Yet Eric Campbell Lam (Contact Author)

Hong Kong Monetary Authority - Hong Kong Institute for Monetary Research (HKIMR) ( email )

55/F, Two International Finance Centre,
8 Finance Street, Central
Hong Kong
China

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