Determinants of Default in P2P Lending
By Carlos Serrano-Cinca, Begoña Gutiérrez-Nieto, Luz López-Palacios. 2015
295 citations recorded by the source corpus.
This paper studies P2P lending and the factors explaining loan default. This is an important issue because in P2P lending individual investors bear the credit risk, instead of financial institutions, which are experts in dealing with this risk. P2P lenders suffer a severe problem of information asymmetry, because they are at a disadvantage facing the borrower. For this reason, P2P lending sites provide potential lenders with information about borrowers and their loan purpose. They also assign a grade to each loan. The empirical study is based on loans’ data collected from Lending Club (N = 24,449) from 2008 to 2014 that are first analyzed by using univariate means tests and survival analysis. Factors explaining default are loan purpose, annual income, current housing situation, credit history and indebtedness. Secondly, a logistic regression model is developed to predict defaults. The grade assigned by the P2P lending site is the most predictive factor of default, but the accuracy of the model is improved by adding other information, especially the borrower’s debt level.
Subfields: Economics and Econometrics, Finance, Management Information Systems
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Citations in the corpus
- Decision support from financial disclosures with deep neural networks and transfer learning (2017, methodology): “4 We thus follow [37, 38] and split the training and test sets in chronological order.”
- The emergence of the global fintech market: economic and technological determinants (2016, background): “In the area of crowdlending, scholars have analyzed the geography of investor behavior (Lin and Viswanathan, 2015), the likelihood of loan defaults (Serrano-Cinca et al., 2015; Iyer et al., 2016), and investors’ privacy preferences when making an investment decision (Burtch et al., 2015).”