Lending Club meets Zillow: local housing prices and default risk of peer-to-peer loans

2022 ◽  
pp. 1-12
Author(s):  
Lijia Mo ◽  
James Yae
Keyword(s):  
Default Risk ◽  
Housing Prices ◽  
Peer To Peer

scholarly journals Predicting Default Risk on Peer-to-Peer Lending Imbalanced Datasets

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Yen-Ru Chen ◽  
Jenq-Shiou Leu ◽  
Sheng-An Huang ◽  
Jui-Tang Wang ◽  
Jun-Ichi Takada
Keyword(s):  
Default Risk ◽  
Peer To Peer ◽  
Imbalanced Datasets ◽  
Peer Lending

scholarly journals Peer-to-Peer Lending – A (Financial Stability) Risk Perspective

2018 ◽  
Vol 69 (1) ◽  
pp. 1-25 ◽  
Author(s):  
Benjamin Käfer
Keyword(s):  
Default Risk ◽  
Financial Stability ◽  
Liquidity Risk ◽  
Peer To Peer ◽  
Soft Information ◽  
Final Conclusion ◽  
Survey Article ◽  
P2p Lending ◽  
Over Time

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.

scholarly journals Comparing Performance of Machine Learning Algorithms for Default Risk Prediction in Peer to Peer Lending

TEM Journal ◽  
2021 ◽  
pp. 133-143
Author(s):  
Yanka Aleksandrova
Keyword(s):  
Machine Learning ◽  
Default Risk ◽  
Credit Scoring ◽  
Learning Algorithms ◽  
Peer To Peer ◽  
Machine Learning Algorithms ◽  
Ensemble Classifiers ◽  
Peer Lending ◽  
Official Site ◽  
Heterogeneous Ensemble

The purpose of this research is to evaluate several popular machine learning algorithms for credit scoring for peer to peer lending. The dataset to fit the models is extracted from the official site of Lending Club. Several models have been implemented, including single classifiers (logistic regression, decision tree, multilayer perceptron), homogeneous ensembles (XGBoost, GBM, Random Forest) and heterogeneous ensemble classifiers like Stacked Ensembles. Results show that ensemble classifiers outperform single ones with Stacked Ensemble and XGBoost being the leaders.

Evaluating borrower’s default risk in peer-to-peer lending: evidence from a lending platform in China

2016 ◽  
Vol 49 (35) ◽  
pp. 3538-3545 ◽  
Author(s):  
Xuchen Lin ◽  
Xiaolong Li ◽  
Zhong Zheng
Keyword(s):  
Default Risk ◽  
Peer To Peer ◽  
Peer Lending

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

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.

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

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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