p2p借贷系统中的信用风险分析

2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA) Pub Date : 2016-09-01 DOI:10.1109/ICKEA.2016.7803017

K. Vinod, S. Natarajan, S. Keerthana, K. M. Chinmayi, N. Lakshmi

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引用次数: 42

摘要

本文旨在分析“LendingClub”公司P2P借贷系统所涉及的信用风险。与银行存款相比，P2P系统可以让投资者获得更高的投资回报，但随之而来的是贷款和利息无法偿还的风险。集成机器学习算法和预处理技术用于探索，分析和确定在预测“LendingClub”公开的2013-2015年贷款申请数据集中涉及的信用风险中发挥关键作用的因素。如果贷款按时有息偿还，就被认为是“好的”贷款。算法经过优化，有利于潜在的良好贷款，同时识别违约或风险信贷。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Credit Risk Analysis in Peer-to-Peer Lending System

This research paper aims to analyze the credit risk involved in peer-to-peer (P2P) lending system of “LendingClub” Company. The P2P system allows investors to get significantly higher return on investment as compared to bank deposit, but it comes with a risk of the loan and interest not being repaid. Ensemble machine learning algorithms and preprocessing techniques are used to explore, analyze and determine the factors which play crucial role in predicting the credit risk involved in “LendingClub” publicly available 2013-2015 loan applications dataset. A loan is considered “good” if it's repaid with interest and on time. The algorithms are optimized to favor the potential good loans whilst identifying defaults or risky credits.

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2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA)

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