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DOI:10.1007/s10479-017-2668-z - Corpus ID: 46940854
@article{Jiang2017LoanDP, title={Loan default prediction by combining soft information extracted from descriptive text in online peer-to-peer lending}, author={Cuiqing Jiang and Zhao Wang and Ruiya Wang and Yong Ding}, journal={Annals of Operations Research}, year={2017}, volume={266}, pages={511 - 529}, url={https://api.semanticscholar.org/CorpusID:46940854}}
- Cuiqing Jiang, Zhao Wang, Yong Ding
- Published in Annals of Operations Research 4 October 2017
- Computer Science, Business
An empirical analysis using real-word data from a major P2P lending platform in China shows that the proposed default prediction method can improve loan default prediction performance compared with existing methods based only on hard information.
118 Citations
5
34
21
2
Topics
Default Prediction Method (opens in a new tab)Loan (opens in a new tab)Peer-to-peer (opens in a new tab)Topic Models (opens in a new tab)Borrowers (opens in a new tab)Online Peer-to-peer (opens in a new tab)Online Peer-to-Peer Lending (opens in a new tab)Two-stage Methods (opens in a new tab)
118 Citations
- Yufei XiaLingyun HeYinguo LiNana LiuYanlin Ding
- 2019
Computer Science, Business
Journal of Forecasting
A novel credit scoring model, which forecasts the probability of default for each applicant and guides the lenders' decision‐making in P2P lending, and utilizes an advanced gradient boosting decision tree technique to predict default loans.
- 60
- Weiguo ZhangChao WangYue ZhangJunbo Wang
- 2020
Computer Science, Business
Electron. Commer. Res. Appl.
- 22
- Kun LiangJun He
- 2020
Computer Science, Business
Electron. Commer. Res. Appl.
- 27
- Mario Sanz-GuerreroJavier Arroyo
- 2024
Computer Science, Business
ArXiv
A novel approach to address the challenge of information asymmetry in P2P lending by leveraging the textual descriptions provided by borrowers during the loan application process, using a Large Language Model (LLM).
- Zhao WangCuiqing JiangHuimin ZhaoYong Ding
- 2020
Computer Science, Business
J. Manag. Inf. Syst.
A novel text mining method for automatically extracting semantic soft factors from descriptive loan texts that contributed to significant improvement on credit risk evaluation in terms of both discrimination performance and granting performance is proposed.
- 56
- Jong Wook LeeWon Kyung LeeS. Sohn
- 2021
Computer Science, Business
Expert Syst. Appl.
- 34
- J. KriebelLennart Stitz
- 2022
Computer Science, Business
Eur. J. Oper. Res.
This work employs deep learning and several other techniques to extract credit-relevant information from user-generated text on Lending Club to show that even short pieces of user- generated text can improve credit default predictions significantly.
- 25
- M. PapouskovaP. Hájek
- 2019
Computer Science, Business
KES-IDT
A novel decision support system to LGD modelling in P2P lending using random forest (RF) learning in two stages to reduce the problem of overfitting and it is demonstrated that the proposed system is effective for the benchmark of P 2P Lending Club platform as other methods currently used inLGD modelling are outperformed.
- 5
- Ji-Yoon KimSung-Bae Cho
- 2018
Computer Science
SOCO-CISIS-ICEUTE
A deep dense convolutional networks (DenseNet) for default prediction in P2P social lending to automatically extract features and improve the performance and the usefulness of the proposed method is demonstrated as the 5-fold cross-validation to evaluate the performance.
- Beibei NiuJinzheng RenXiaotao Li
- 2019
Computer Science, Business
Inf.
The machine learning algorithm results show that social network information can improve loan default prediction performance significantly and suggest thatsocial network information is valuable for credit scoring.
- 34
- Highly Influenced[PDF]
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36 References
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- 2015
Economics, Business
Online Peer-to-Peer (P2P) lending has emerged recently. This micro loan market could offer certain benefits to both borrowers and lenders. Using data from the Lending Club, which is one of the…
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Business, Computer Science
Int. J. Cogn. Informatics Nat. Intell.
Using textual information can improve the performance of credit risk evaluation system when combined with traditional financial information.
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Business, Computer Science
Eur. J. Oper. Res.
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Economics, Business
Using a unique, hand‐collected database of 389 small loans granted by a French social bank dealing with genuinely small, informationally opaque businesses (mainly social enterprises), our study…
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- Mingfeng LinN. PrabhalaS. Viswanathan
- 2013
Economics, Business
Manag. Sci.
It is found that the online friendships of borrowers act as signals of credit quality and increase the probability of successful funding, lower interest rates on funded loans, and are associated with lower ex post default rates.
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Economics
This article examines whether urban micro-finance institutions (MFIs) consider proxy/hidden collateral in the absence of physical as well as social collateral in judging the creditworthiness of a…
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- P. HájekKrzysztof Michalak
- 2013
Business, Computer Science
Knowl. Based Syst.
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- Rajkamal IyerA. KhwajaErzo F. P. LuttmerK. Shue
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Economics
Manag. Sci.
This paper examines the performance of new online lending markets that rely on nonexpert individuals to screen their peers' creditworthiness. We find that these peer lenders predict an individual's…
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