Analysis and prediction of P2P online lending platform - Based on binary logistic regression model

Yuanyuan Zhang; Yuelin Shen

Analysis and prediction of P2P online lending platform - Based on binary logistic regression model

Yuanyuan Zhang^*, Yuelin Shen

^*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

Abstract

This paper uses 17 indicators to analyze the critical factors that affect the P2P online lending platforms based on a binary logistic regression model. It shows that the problematic P2P online lending platforms are closely related to four indicators: the average interest rate, the top ten borrowers in terms of the repayment amount, the operating duration, and the top ten investors to receive the repayments. The accuracy of the regression model is up to 73% to predict whether the P2P network lending platform will be in a problem.

Original language	English
Title of host publication	2017 7th International Workshop on Computer Science and Engineering, WCSE 2017
Publisher	International Workshop on Computer Science and Engineering (WCSE)
Pages	1289-1295
Number of pages	7
ISBN (Electronic)	9789811136719
Publication status	Published - 2017
Externally published	Yes
Event	2017 7th International Workshop on Computer Science and Engineering, WCSE 2017 - Beijing, China Duration: 25 Jun 2017 → 27 Jun 2017

Publication series

Name	2017 7th International Workshop on Computer Science and Engineering, WCSE 2017

Conference

Conference	2017 7th International Workshop on Computer Science and Engineering, WCSE 2017
Country/Territory	China
City	Beijing
Period	25/06/17 → 27/06/17

Keywords

Binary logistic regression model
Online loan
Peer-to-peer
Problematic platform

Cite this

Zhang, Y., & Shen, Y. (2017). Analysis and prediction of P2P online lending platform - Based on binary logistic regression model. In 2017 7th International Workshop on Computer Science and Engineering, WCSE 2017 (pp. 1289-1295). (2017 7th International Workshop on Computer Science and Engineering, WCSE 2017). International Workshop on Computer Science and Engineering (WCSE).

Zhang, Yuanyuan ; Shen, Yuelin. / Analysis and prediction of P2P online lending platform - Based on binary logistic regression model. 2017 7th International Workshop on Computer Science and Engineering, WCSE 2017. International Workshop on Computer Science and Engineering (WCSE), 2017. pp. 1289-1295 (2017 7th International Workshop on Computer Science and Engineering, WCSE 2017).

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title = "Analysis and prediction of P2P online lending platform - Based on binary logistic regression model",

abstract = "This paper uses 17 indicators to analyze the critical factors that affect the P2P online lending platforms based on a binary logistic regression model. It shows that the problematic P2P online lending platforms are closely related to four indicators: the average interest rate, the top ten borrowers in terms of the repayment amount, the operating duration, and the top ten investors to receive the repayments. The accuracy of the regression model is up to 73% to predict whether the P2P network lending platform will be in a problem.",

keywords = "Binary logistic regression model, Online loan, Peer-to-peer, Problematic platform",

author = "Yuanyuan Zhang and Yuelin Shen",

note = "Funding Information: This project is in part supported by National Science Foundation of China (Grant Number: 71371114).; 2017 7th International Workshop on Computer Science and Engineering, WCSE 2017 ; Conference date: 25-06-2017 Through 27-06-2017",

year = "2017",

language = "English",

series = "2017 7th International Workshop on Computer Science and Engineering, WCSE 2017",

publisher = "International Workshop on Computer Science and Engineering (WCSE)",

pages = "1289--1295",

booktitle = "2017 7th International Workshop on Computer Science and Engineering, WCSE 2017",

}

Zhang, Y & Shen, Y 2017, Analysis and prediction of P2P online lending platform - Based on binary logistic regression model. in 2017 7th International Workshop on Computer Science and Engineering, WCSE 2017. 2017 7th International Workshop on Computer Science and Engineering, WCSE 2017, International Workshop on Computer Science and Engineering (WCSE), pp. 1289-1295, 2017 7th International Workshop on Computer Science and Engineering, WCSE 2017, Beijing, China, 25/06/17.

Analysis and prediction of P2P online lending platform - Based on binary logistic regression model. / Zhang, Yuanyuan; Shen, Yuelin.
2017 7th International Workshop on Computer Science and Engineering, WCSE 2017. International Workshop on Computer Science and Engineering (WCSE), 2017. p. 1289-1295 (2017 7th International Workshop on Computer Science and Engineering, WCSE 2017).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

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AU - Shen, Yuelin

N1 - Funding Information: This project is in part supported by National Science Foundation of China (Grant Number: 71371114).

PY - 2017

Y1 - 2017

N2 - This paper uses 17 indicators to analyze the critical factors that affect the P2P online lending platforms based on a binary logistic regression model. It shows that the problematic P2P online lending platforms are closely related to four indicators: the average interest rate, the top ten borrowers in terms of the repayment amount, the operating duration, and the top ten investors to receive the repayments. The accuracy of the regression model is up to 73% to predict whether the P2P network lending platform will be in a problem.

AB - This paper uses 17 indicators to analyze the critical factors that affect the P2P online lending platforms based on a binary logistic regression model. It shows that the problematic P2P online lending platforms are closely related to four indicators: the average interest rate, the top ten borrowers in terms of the repayment amount, the operating duration, and the top ten investors to receive the repayments. The accuracy of the regression model is up to 73% to predict whether the P2P network lending platform will be in a problem.

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KW - Online loan

KW - Peer-to-peer

KW - Problematic platform

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BT - 2017 7th International Workshop on Computer Science and Engineering, WCSE 2017

PB - International Workshop on Computer Science and Engineering (WCSE)

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Y2 - 25 June 2017 through 27 June 2017

ER -

Analysis and prediction of P2P online lending platform - Based on binary logistic regression model

Abstract

Publication series

Conference

Keywords

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