NDLI: A Data-Driven Approach to Predict Default Risk of Loan for Online Peer-to-Peer (P2P) Lending

Content Provider	IEEE Xplore Digital Library
Author	Yu Jin Yudan Zhu
Copyright Year	2015
Description	Author affiliation: Sch. of Inf, Zhejiang Univ. of Finance & Econ., Hangzhou, China (Yu Jin; Yudan Zhu)
Abstract	Online Peer-to-Peer (P2P) lending has achieved explosive development recently, which could be beneficial to both sides of individual lending. In this study, a data mining (DM) approach to predict the performance of P2P loan before funded is proposed. Using data from the Lending Club, we explore the characteristics of loan and its applicant and use random forest to do the feature selection in the modeling phase. The Difference from other risk prediction models is that the prediction is classified into three or four categories, rather than just two the default and not default classes. Then we compare five DM models: two decision trees (DTs), two neural networks (NNs) and one support vector machine (SVM) and use two metrics: average percent hit rate and area of the lift cumulative curve to evaluate the prediction results. The Empirical result shows that the term of loan, annual income, the amount of loan, debt-to-income ratio, credit grade and revolving line utilization play an important role in loan defaults. And SVM, Classification and Regression Tree (CART) and Multi-layer perceptron (MPL)'s prediction performance are almost equal.
Sponsorship	IETE Gwalior Sub Center
Starting Page	609
Ending Page	613
File Size	294315
Page Count	5
File Format	PDF
e-ISBN	9781479917976
DOI	10.1109/CSNT.2015.25
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-04-04
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Biological system modeling Artificial neural networks Predictive models MPL Random forest SVM Data mining Support vector machines Accuracy Peer-to-Peer lending Peer-to-peer computing Default risk CART
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A Framework for P2P Botnet Detection Using SVM

Distributed privacy-preserving P2P data mining via probabilistic neural network committee machines

Predicting Credit Risk in Peer-to-Peer Lending: A Neural Network Approach

Determinants of Borrowers' Default in P2P Lending under Consideration of the Loan Risk Class

Active Learning-Based Pedagogical Rule Extraction

Research of SVM Applying in the Risk of Bank's Loan to Enterprises

Data Mining in Building Behavioral Scoring Models

Determinants of borrowers' default in P2P lending under consideration of the loan risk class

Modeling consumer loan default prediction using ensemble neural networks

A Data-Driven Approach to Predict Default Risk of Loan for Online Peer-to-Peer (P2P) Lending

Similar Documents

A Framework for P2P Botnet Detection Using SVM

Distributed privacy-preserving P2P data mining via probabilistic neural network committee machines

Predicting Credit Risk in Peer-to-Peer Lending: A Neural Network Approach

Determinants of Borrowers' Default in P2P Lending under Consideration of the Loan Risk Class

Active Learning-Based Pedagogical Rule Extraction

Research of SVM Applying in the Risk of Bank's Loan to Enterprises

Data Mining in Building Behavioral Scoring Models

Determinants of borrowers' default in P2P lending under consideration of the loan risk class

Modeling consumer loan default prediction using ensemble neural networks

A Data-Driven Approach to Predict Default Risk of Loan for Online Peer-to-Peer (P2P) Lending