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首页> 外文期刊>International journal of software science and computational intelligence >Feature Engineering for Credit Risk Evaluation in Online P2P Lending
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Feature Engineering for Credit Risk Evaluation in Online P2P Lending

机译：在线P2P借贷中的信用风险评估功能工程

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摘要

The rise of online P2P lending, as a novel economic lending model, brings new opportunities and challenges for the research of credit risk evaluation. This paper aims to mine information from different data sources to improve the performance of credit risk evaluation models. Be-sides the personal financial and demographic data used in traditional models, the authors collect in-formation from (1) text description, (2) social network and (3) macro-economic data. They de-sign methods to extract features from unstructured data. To avoid the curse of dimensionality caused by too many features and identify the key factors in credit risk, the authors remove the irrelevant and redundant features by feature selection. Using the data provided by Prosper.com, one of the biggest P2P lending platforms in the world, they show that: (1) it can achieve better performance, measured by both AUC (area under the receiver operating characteristic curve) and classification accuracy, by fusion of information from different data sources; (2) it requires only ten features from different data sources to get better performance.
机译：在线P2P借贷作为一种新型的经济借贷模式的兴起，为信用风险评估研究带来了新的机遇和挑战。本文旨在挖掘来自不同数据源的信息，以改善信用风险评估模型的性能。除了传统模型中使用的个人财务和人口统计数据外，作者还从（1）文字说明，（2）社交网络和（3）宏观经济数据中收集信息。他们设计了从非结构化数据中提取特征的方法。为了避免因过多的特征引起的尺寸诅咒并确定信用风险中的关键因素，作者通过特征选择去除了不相关和多余的特征。利用全球最大的P2P借贷平台之一Prosper.com提供的数据，他们表明：（1）通过AUC（在接收者操作特征曲线下的面积）和分类准确性来衡量，它可以获得更好的性能，通过融合来自不同数据源的信息； （2）仅需要来自不同数据源的十项功能即可获得更好的性能。

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