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Forward looking vs backward looking

Forward looking vs backward looking PurposeThe purpose of this paper is to estimate the effectiveness of the credit evaluation system using the borrowing data from China’s leading P2P lending platform, Renrendai.com.Design/methodology/approachThe current credit valuation systems are classified into the forward-looking mechanism, which judges the borrowers’ credit levels based on their uploaded information, and the backward-looking mechanism, which judges the borrowers’ credit levels based on their historical repayment performance. Probit models and Tobit models are used to examine the effectiveness of credit evaluation mechanisms.FindingsThe results show that only the “hard” information reflecting borrowers’ credit ability can explain the default risk on the platform under the forward-looking credit evaluation mechanism. The backward-looking credit evaluation mechanism (BCEM) based on the repeated borrowings produces both promise-enhancing and “fishing” incentives and thus fails to explain the default risk, and weakens the effectiveness of forward-looking credit indicators in explaining the default risk because it encourages borrowers to invest in forging forward-looking credit indicators. Additional information such as the interest rate and the repayment periods reveals borrowers’ credit and thus can also be used as a predictor of borrowers’ default risk.Practical implicationsThe findings suggest that current ex ante screening based on the information collected from the borrowers or repeated borrowings is inadequate to control the default risk in P2P lending markets and thus needs be improved. Ex post monitoring and sharing on defaulter’s information should be strengthened to increase the default cost and thus to deter potential bad borrowers.Originality/valueTo the authors’ knowledge, this is the first paper classifying the credit evaluation system in online P2P lending market into the forward-looking type and the backward-looking type, which is important since they provide different incentives to borrowers. The paper also investigates and provides evidence on the promise-enhancing and “fishing” incentives of BCEMs. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png China Finance Review International Emerald Publishing

Forward looking vs backward looking

Gao, Yanyan; Sun, Jun; Zhou, Qin
China Finance Review International , Volume 7 (2): 21 – May 15, 2017
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21 pages

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
2044-1398
DOI
10.1108/CFRI-07-2016-0089
Publisher site
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Abstract

PurposeThe purpose of this paper is to estimate the effectiveness of the credit evaluation system using the borrowing data from China’s leading P2P lending platform, Renrendai.com.Design/methodology/approachThe current credit valuation systems are classified into the forward-looking mechanism, which judges the borrowers’ credit levels based on their uploaded information, and the backward-looking mechanism, which judges the borrowers’ credit levels based on their historical repayment performance. Probit models and Tobit models are used to examine the effectiveness of credit evaluation mechanisms.FindingsThe results show that only the “hard” information reflecting borrowers’ credit ability can explain the default risk on the platform under the forward-looking credit evaluation mechanism. The backward-looking credit evaluation mechanism (BCEM) based on the repeated borrowings produces both promise-enhancing and “fishing” incentives and thus fails to explain the default risk, and weakens the effectiveness of forward-looking credit indicators in explaining the default risk because it encourages borrowers to invest in forging forward-looking credit indicators. Additional information such as the interest rate and the repayment periods reveals borrowers’ credit and thus can also be used as a predictor of borrowers’ default risk.Practical implicationsThe findings suggest that current ex ante screening based on the information collected from the borrowers or repeated borrowings is inadequate to control the default risk in P2P lending markets and thus needs be improved. Ex post monitoring and sharing on defaulter’s information should be strengthened to increase the default cost and thus to deter potential bad borrowers.Originality/valueTo the authors’ knowledge, this is the first paper classifying the credit evaluation system in online P2P lending market into the forward-looking type and the backward-looking type, which is important since they provide different incentives to borrowers. The paper also investigates and provides evidence on the promise-enhancing and “fishing” incentives of BCEMs.

Journal

China Finance Review International – Emerald Publishing

Published: May 15, 2017

References

  • Is crowdfunding different? Evidence on the relation between gender and funding success from a German peer-to-peer lending platform
  • Emergence of financial intermediaries in electronic markets: the case of online P2P lending
  • Search and herding effects in peer-to-peer lending: evidence from Prosper.com
  • The role of collateral in credit markets with imperfect information
  • Relationship banking: what do we know?