Improving Credit Risk Prediction in Online Peer-to-Peer (P2P) Lending Using Feature selection with Deep learning

2019 ◽  
Author(s):  
Van-Sang Ha ◽  
Dang-Nhac Lu ◽  
Gyoo Seok Choi ◽  
Ha-Nam Nguyen ◽  
Byeongnam Yoon
Keyword(s):  
Feature Selection ◽  
Deep Learning ◽  
Credit Risk ◽  
Risk Prediction ◽  
Peer To Peer ◽  
P2p Lending

scholarly journals Towards Repayment Prediction in Peer-to-Peer Social Lending Using Deep Learning

Mathematics ◽  
2019 ◽  
Vol 7 (11) ◽  
pp. 1041 ◽  
Author(s):  
Kim ◽  
Cho
Keyword(s):  
Deep Learning ◽  
Design Feature ◽  
Feature Space ◽  
Peer To Peer ◽  
Complex Data ◽  
Complex Information ◽  
Social Lending ◽  
P2p Lending ◽  
Complex Features ◽  
Deep Learning Model

Peer-to-Peer (P2P) lending transactions take place by the lenders choosing a borrower and lending money. It is important to predict whether a borrower can repay because the lenders must bear the credit risk when the borrower defaults, but it is difficult to design feature extractors with very complex information about borrowers and loan products. In this paper, we present an architecture of deep convolutional neural network (CNN) for predicting the repayment in P2P social lending to extract features automatically and improve the performance. CNN is a deep learning model for classifying complex data, which extracts discriminative features automatically by convolution operation on lending data. We classify the borrower’s loan status by capturing the robust features and learning the patterns. Experimental results with 5-fold cross-validation show that our method automatically extracts complex features and is effective in repayment prediction on Lending Club data. In comparison with other machine learning methods, the standard CNN has achieved the highest performance with 75.86%. Exploiting various CNN models such as Inception, ResNet, and Inception-ResNet results in the state-of-the-art performance of 77.78%. We also demonstrate that the features extracted by our model are better performed by projecting the samples into the feature space.

Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending

2014 ◽  
Vol 47 (1) ◽  
pp. 54-70 ◽  
Author(s):  
Riza Emekter ◽  
Yanbin Tu ◽  
Benjamas Jirasakuldech ◽  
Min Lu
Keyword(s):  
Credit Risk ◽  
Peer To Peer ◽  
P2p 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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