Credit Default Prediction from User-Generated Text in Peer-to-Peer Lending Using Deep Learning.

2021 ◽  
Author(s):  
Johannes Kriebel ◽  
Lennart Stitz
Keyword(s):  
Deep Learning ◽  
Peer To Peer ◽  
Default Prediction ◽  
Credit Default ◽  
Peer Lending

scholarly journals A Deep Learning Approach for Credit Scoring of Peer-to-Peer Lending Using Attention Mechanism LSTM

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 2161-2168 ◽  
Author(s):  
Chongren Wang ◽  
Dongmei Han ◽  
Qigang Liu ◽  
Suyuan Luo
Keyword(s):  
Deep Learning ◽  
Credit Scoring ◽  
Peer To Peer ◽  
Attention Mechanism ◽  
Learning Approach ◽  
Peer Lending

scholarly journals Heterogeneous Ensemble for Default Prediction of Peer-to-Peer Lending in China

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 54396-54406 ◽  
Author(s):  
Wei Li ◽  
Shuai Ding ◽  
Yi Chen ◽  
Shanlin Yang
Keyword(s):  
Peer To Peer ◽  
Default Prediction ◽  
Peer Lending ◽  
Heterogeneous Ensemble

Heterogeneous ensemble learning with feature engineering for default prediction in peer-to-peer lending in China

2019 ◽  
Vol 23 (1) ◽  
pp. 23-45 ◽  
Author(s):  
Wei Li ◽  
Shuai Ding ◽  
Hao Wang ◽  
Yi Chen ◽  
Shanlin Yang
Keyword(s):  
Ensemble Learning ◽  
Peer To Peer ◽  
Feature Engineering ◽  
Default Prediction ◽  
Peer Lending ◽  
Heterogeneous Ensemble

scholarly journals Wide and deep learning for peer-to-peer lending

2019 ◽  
Vol 134 ◽  
pp. 209-224 ◽  
Author(s):  
Kaveh Bastani ◽  
Elham Asgari ◽  
Hamed Namavari
Keyword(s):  
Deep Learning ◽  
Peer To Peer ◽  
Peer Lending

Loan default prediction by combining soft information extracted from descriptive text in online peer-to-peer lending

2017 ◽  
Vol 266 (1-2) ◽  
pp. 511-529 ◽  
Author(s):  
Cuiqing Jiang ◽  
Zhao Wang ◽  
Ruiya Wang ◽  
Yong Ding
Keyword(s):  
Peer To Peer ◽  
Soft Information ◽  
Loan Default ◽  
Default Prediction ◽  
Peer Lending

scholarly journals Accessing Information Asymmetry in Peer-to-Peer Lending by Default Prediction from Investors’ Perspective

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 935
Author(s):  
Xinyuan Wei ◽  
Bo Yu ◽  
Yao Liu
Keyword(s):  
Information Asymmetry ◽  
Risk Preferences ◽  
Peer To Peer ◽  
Rapid Expansion ◽  
Default Prediction ◽  
Investment Opportunities ◽  
Out Of Sample ◽  
Sample Test ◽  
Peer Lending ◽  
The Impact

Recent a few years have witnessed the rapid expansion of the peer-to-peer lending marketplace. As a new field of investment and a novel channel of financing, it has drawn extensive attention throughout the world. Many investors have shown great enthusiasm for this field. However, investors are at the disadvantage of information asymmetry, which is a key issue in this marketplace that is unavoidable and can lead to moral hazard or adverse selection. In this paper, we propose an L 1 / 2 -regularized weighted logistic regression model for default prediction of peer-to-peer lending loans from investors’ perspective, which can reduce the impact of information asymmetry in the process of loan decision. Rather than solely focus on the accuracy of the prediction, we take into consideration the different risk preferences of different investors. We try to find a trade-off between the risk of losing principal and that of losing potential investment opportunities on the basis of investors’ risk preferences. Meanwhile, due to the nature of peer-to-peer lending loans, we add an L 1 / 2 -regularization term to reduce the chance of overfitting. Xu’s algorithm for L 1 / 2 -regularization problems is applied to solve our model. We perform training, in-sample test, and out-of-sample test with data from LendingClub. Numerical experiments demonstrate that regularization could enhance out-of-sample the area under the Precision–Recall curve (AUPRC). By applying the proposed model, the risk-averse investors could apply a higher penalty factor to lower the risk of losing principal at the cost of the loss of some potential investment opportunities according to their own risk preferences. This model can help investors reduce the impact of information asymmetry to a great extent.

A Deep Learning Approach to Competing Risks Representation in Peer-to-Peer Lending

2019 ◽  
Vol 30 (5) ◽  
pp. 1565-1574 ◽  
Author(s):  
Fei Tan ◽  
Xiurui Hou ◽  
Jie Zhang ◽  
Zhi Wei ◽  
Zhenyu Yan
Keyword(s):  
Deep Learning ◽  
Competing Risks ◽  
Peer To Peer ◽  
Learning Approach ◽  
Peer Lending
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