Loan default prediction by combining soft information extracted from descriptive text in online peer-to-peer lending | Semantic Scholar (2024)

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Topics 118 Citations 36 References Related Papers

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@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

Highly Influential Citations

5

Background Citations

34

Methods Citations

21

Results Citations

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

Predicting loan default in peer‐to‐peer lending using narrative data
    Yufei XiaLingyun HeYinguo LiNana LiuYanlin Ding

    Computer Science, Business

    Journal of Forecasting

  • 2019

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
Analyzing credit risk among Chinese P2P-lending businesses by integrating text-related soft information
    Kun LiangJun He

    Computer Science, Business

    Electron. Commer. Res. Appl.

  • 2020
  • 27
Credit Risk Meets Large Language Models: Building a Risk Indicator from Loan Descriptions in P2P Lending
    Mario Sanz-GuerreroJavier Arroyo

    Computer Science, Business

    ArXiv

  • 2024

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).

Mining Semantic Soft Factors for Credit Risk Evaluation in Peer-to-Peer Lending
    Zhao WangCuiqing JiangHuimin ZhaoYong Ding

    Computer Science, Business

    J. Manag. Inf. Syst.

  • 2020

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
Graph convolutional network-based credit default prediction utilizing three types of virtual distances among borrowers
    Jong Wook LeeWon Kyung LeeS. Sohn

    Computer Science, Business

    Expert Syst. Appl.

  • 2021
  • 34
Credit Default Prediction from User-Generated Text in Peer-to-Peer Lending Using Deep Learning
    J. KriebelLennart Stitz

    Computer Science, Business

    Eur. J. Oper. Res.

  • 2022

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
Modelling Loss Given Default in Peer-to-Peer Lending Using Random Forests
    M. PapouskovaP. Hájek

    Computer Science, Business

    KES-IDT

  • 2019

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
Deep Dense Convolutional Networks for Repayment Prediction in Peer-to-Peer Lending
    Ji-Yoon KimSung-Bae Cho

    Computer Science

    SOCO-CISIS-ICEUTE

  • 2018

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.

Credit Scoring Using Machine Learning by Combing Social Network Information: Evidence from Peer-to-Peer Lending
    Beibei NiuJinzheng RenXiaotao Li

    Computer Science, Business

    Inf.

  • 2019

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.

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36 References

Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending
    Riza EmekterYanbin TuB. JirasakuldechMin Lu

    Economics, Business

  • 2015

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…

  • 428
Description-text related soft information in peer-to-peer lending – Evidence from two leading European platforms
    G. DorfleitnerChristopher Priberny Julia Kammler

    Economics, Computer Science

  • 2016
  • 231
  • PDF
Risk assessment in social lending via random forests
    Milad MalekipirbazariV. Aksakalli

    Computer Science, Economics

    Expert Syst. Appl.

  • 2015
  • 319
Credit Risk Evaluation Based on Text Analysis
    Shuxia WangYuwei QiBin FuHongzhi Liu

    Business, Computer Science

    Int. J. Cogn. Informatics Nat. Intell.

  • 2016

Using textual information can improve the performance of credit risk evaluation system when combined with traditional financial information.

  • 12
Instance-based credit risk assessment for investment decisions in P2P lending
    Yanhong GuoWenjun ZhouChunyu LuoChuanren LiuHui Xiong

    Business, Computer Science

    Eur. J. Oper. Res.

  • 2016
  • 263
The Relevance of Soft Information for Predicting Small Business Credit Default: Evidence from a Social Bank
    S. Cornée

    Economics, Business

  • 2019

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…

  • 67
  • PDF
Judging Borrowers by the Company They Keep: Friendship Networks and Information Asymmetry in Online Peer-to-Peer Lending
    Mingfeng LinN. PrabhalaS. Viswanathan

    Economics, Business

    Manag. Sci.

  • 2013

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.

  • 1,084
  • PDF
Creditworthiness of a Borrower and the Selection Process in Micro-finance: A Case Study from the Urban Slums of India
    S. Paul

    Economics

  • 2013

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…

  • 7
  • PDF
Feature selection in corporate credit rating prediction
    P. HájekKrzysztof Michalak

    Business, Computer Science

    Knowl. Based Syst.

  • 2013
  • 96
  • PDF
Screening Peers Softly: Inferring the Quality of Small Borrowers
    Rajkamal IyerA. KhwajaErzo F. P. LuttmerK. Shue

    Economics

    Manag. Sci.

  • 2016

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…

  • 459
  • PDF

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