A Data-Driven Approach to Predict Default Risk of Loan for Online Peer-to-Peer (P2P) Lending

AbstractThe aim of this survey article is to discuss P2P lending, a subcategory of crowdfunding, from a (financial stability) risk perspective. The discussion focuses on a number of dimensions such as the role of soft information, herding, platform default risk, liquidity risk, and the institutionalization of P2P markets. Overall, we conclude that P2P lending is more risky than traditional banking. However, it is important to recognize that a constant conclusion would be misleading. P2P platforms have evolved and changed their appearance markedly over time, which implies that although our final conclusion of increased riskiness through P2P markets remains valid over time, it is based on different arguments at different points in time.

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A DBM-Based Ensemble Method for Improving Default Risk Prediction of Peer-to-Peer (P2P) Lending

International Journal of e-Education e-Business e-Management and e-Learning ◽

10.17706/ijeeee.2021.11.3.85-92 ◽

2021 ◽

Vol 11 (3) ◽

pp. 85-92

Author(s):

Shan Gao ◽

◽

Xuefeng Wang

Keyword(s):

Risk Prediction ◽

Default Risk ◽

Influencing Factor ◽

Real Data ◽

Peer To Peer ◽

Ensemble Method ◽

Real World Data ◽

The Real ◽

Healthy Development ◽

P2p Lending

With the peer-to-peer lending (P2P) business growing up, the most important influencing factor for the healthy development of this industry is the default risk of borrowers. Because the behavior between lenders and borrowers is real time, naturally large amounts of transaction data are being generated all the time. However, it is difficult to extract useful representative features and choose an appropriate model to predict the default risk of the borrowing behavior. In this paper, we proposed a (Deep Boltzmann Machines) DBM-based ensemble method for the default risk prediction in p2p lending, which is based on the real data generated by Lending Club company. Experimental results on the real world data indicate that our model is more effective and powerful with a 0.9093 explanation power.

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Gender effect on the default risk in peer-to-peer lending markets: The case of the largest Chinese platform

Risk Governance and Control Financial Markets & Institutions ◽

10.22495/rgcv9i3p1 ◽

2019 ◽

Vol 9 (3) ◽

pp. 8-22

Author(s):

Lin Lingnan

Keyword(s):

Credit Risk ◽

Gender Discrimination ◽

Default Risk ◽

Probit Model ◽

Peer To Peer ◽

Gender Effect ◽

Individual Characteristics ◽

Model Estimate ◽

P2p Lending ◽

Peer Lending

Research of gender effect on funding success in peer-to-peer lending markets demonstrates that gender discrimination is a platform-specific phenomenon rather than a common feature. Can we get a similar conclusion about the relationship between gender and credit risk? How do gender differences affect default risk? We try to answer this question using the data of the largest peer-to-peer lending platform RenRenDai spanning from March 2016 to September 2016. In order to avoid the endogeneity problem, this paper first uses the instrumental variable method to conduct a baseline Probit model estimate connecting gender difference to the default rate with several borrowers’ individual characteristics under control. Then the original Probit model and a propensity score matching method aiming to eliminate the effects of divergent observable characteristics are applied to test the robustness of the outcome. Both the baseline estimation and the robustness test show that there is no significant gender effect on the probability of default, ceteris paribus. Therefore, borrowers’ gender is not a good screening device for the P2P lending platform to control the credit risk; other factors should be taken into account to reduce the non-performing loan rate. However, since this paper only investigates the situation of RenRenDai and the data we use is limited, we should be very careful to generalize our findings to other P2P lending platforms. More research on different P2P lending platforms in different regulatory regimes is in necessity

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Evaluating Default Risk and Loan Performance in UK Peer-to-Peer Lending: Evidence from Funding Circle

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2021.p0530 ◽

2021 ◽

Vol 25 (5) ◽

pp. 530-538

Author(s):

Boyu Xu ◽

◽

Zhifang Su ◽

Jan Celler

Keyword(s):

Interest Rate ◽

Default Risk ◽

Binary Logistic Regression ◽

The United States ◽

Peer To Peer ◽

Loan Default ◽

P2p Lending ◽

Loan Defaults ◽

The Interest Rate ◽

The Uk

The United Kingdom is the third-largest peer-to-peer (P2P) lending market in the world, which is surpassed only by the two dominant forces in P2P investing, China and the United States of America. As an innovative financial market in the UK, P2P lending brings not only many opportunities but also many risks, especially the loan default risk. In this context, this paper uses binary logistic regression and survival analysis to evaluate default risk and loan performance in UK P2P lending. The empirical results indicate that credit group, loan purpose for capital needs, sector type, loan amount, interest rate, loan term, and the age of the company all have a significant impact on the probability of loan default. Among them, the interest rate, loan term, and loan purpose for capital needs are the three most important determinants of the probability of loan defaults and survival time of loans.

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