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Determinants of Default in P2P Lending.

Serrano-Cinca C, Gutiérrez-Nieto B, López-Palacios L - PLoS ONE (2015)

Bottom Line: They also assign a grade to each loan.Factors explaining default are loan purpose, annual income, current housing situation, credit history and indebtedness.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Accounting and Finance, University of Zaragoza, Zaragoza, Spain.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

Relationship between survival functions for the Cox model.
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pone.0139427.g001: Relationship between survival functions for the Cox model.

Mentions: Survival curves can be useful for lenders, because they show the probabilities of default at a certain point of time (Fig 1). The chart at the bottom displays the survival curves for each loan purpose. The chart at the top left displays the survival curves for ‘wedding’ loans. It can be clearly appreciated that the probability of survival is higher for ‘wedding’ purposes than for ‘non-wedding’ purposes. The chart at the top right displays the survival curves for ‘small business’ loans. Here, the probability of survival is lower for ‘small business’ purposes than for ‘no small business” purposes.


Determinants of Default in P2P Lending.

Serrano-Cinca C, Gutiérrez-Nieto B, López-Palacios L - PLoS ONE (2015)

Relationship between survival functions for the Cox model.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4591266&req=5

pone.0139427.g001: Relationship between survival functions for the Cox model.
Mentions: Survival curves can be useful for lenders, because they show the probabilities of default at a certain point of time (Fig 1). The chart at the bottom displays the survival curves for each loan purpose. The chart at the top left displays the survival curves for ‘wedding’ loans. It can be clearly appreciated that the probability of survival is higher for ‘wedding’ purposes than for ‘non-wedding’ purposes. The chart at the top right displays the survival curves for ‘small business’ loans. Here, the probability of survival is lower for ‘small business’ purposes than for ‘no small business” purposes.

Bottom Line: They also assign a grade to each loan.Factors explaining default are loan purpose, annual income, current housing situation, credit history and indebtedness.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Accounting and Finance, University of Zaragoza, Zaragoza, Spain.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus