P2P Borrower Default Identification and Prediction Based on RFE-Multiple Classification Models

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ISSN Print: 2329-3284
ISSN Online: 2329-3292
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Citations

"P2P Borrower Default Identification and Prediction Based on RFE-Multiple Classification Models"
written by Xianyan Hou,
published by Open Journal of Business and Management, Vol.8 No.2, 2020

has been cited by the following article(s):

[1]	Predictive analysis of default risk in peer-to-peer lending platforms: empirical evidence from LendingClub
	Journal of Financial Risk Management, 2023

[2]	Detection of Defaulters in P2P Lending Platforms using Unsupervised Learning
	2022 IEEE International Conference on …, 2022

[3]	Identifying and predicting default borrowers in P2P lending platform: A machine learning approach
	2021 IEEE International …, 2021

[4]	Challenges of Financial Risk Management: AI Applications
	2021

[5]	Peer-to-peer lending loan default prediction: machine learning classification algorithms applied to Lending Club data, investors' perspective
	2020

[1]	Explainable AI based LightGBM prediction model to predict default borrower in social lending platform Intelligent Systems with Applications, 2025 DOI:10.1016/j.iswa.2025.200514

[2]	Artificial Intelligence: The Strategy of Financial Risk Management Finance: Theory and Practice, 2024 DOI:10.26794/2587-5671-2024-28-3-174-182

[3]	Detection of Defaulters in P2P Lending Platforms using Unsupervised Learning 2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS), 2022 DOI:10.1109/COINS54846.2022.9854964

[4]	Identifying and predicting default borrowers in P2P lending platform: A machine learning approach 2021 IEEE International Conference on Social Sciences and Intelligent Management (SSIM), 2021 DOI:10.1109/SSIM49526.2021.9555200

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