• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE International Conference on Tools with Artificial Intelligence >Improving Credit Risk Prediction in Online Peer-to-Peer (P2P) Lending Using Imbalanced Learning Techniques
【24h】

Improving Credit Risk Prediction in Online Peer-to-Peer (P2P) Lending Using Imbalanced Learning Techniques

机译：使用不平衡学习技术改善在线对等（P2P）贷款中的信用风险预测

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Peer-to-peer (P2P) lending is a global trend of financial markets that allow individuals to obtain and concede loans without having financial institutions as a strong proxy. As many real-world applications, P2P lending presents an imbalanced characteristic, where the number of creditworthy loan requests is much larger than the number of non-creditworthy ones. In this work, we wrangle a real-world P2P lending data set from Lending Club, containing a large amount of data gathered from 2007 up to 2016. We analyze how supervised classification models and techniques to handle class imbalance impact creditworthiness prediction rates. Ensembles, cost-sensitive and sampling methods are combined and evaluated along logistic regression, decision tree, and bayesian learning schemes. Results show that, in average, sampling techniques outperform ensembles and cost sensitive approaches.
机译：点对点（P2P）借贷是金融市场的全球趋势，允许个人在没有金融机构作为有力代理的情况下获得和让步贷款。与许多现实世界中的应用程序一样，P2P借贷呈现出不平衡的特征，其中信誉良好的贷款请求的数量大大多于不信誉良好的贷款请求的数量。在这项工作中，我们整理了Lending Club的真实P2P借贷数据集，其中包含从2007年到2016年收集的大量数据。我们分析了有监督的分类模型和技术如何处理类不平衡对信用信誉度的预测率。集成，成本敏感和抽样方法结合起来，并通过逻辑回归，决策树和贝叶斯学习方案进行评估。结果表明，平均而言，抽样技术的表现要优于整体方法和成本敏感的方法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号