英文标题：
《Machine learning application in online lending risk prediction》
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作者：
Xiaojiao Yu
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最新提交年份：
2017
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英文摘要：
Online leading has disrupted the traditional consumer banking sector with more effective loan processing. Risk prediction and monitoring is critical for the success of the business model. Traditional credit score models fall short in applying big data technology in building risk model. In this manuscript, data with various format and size were collected from public website, third-parties and assembled with client\'s loan application information data. Ensemble machine learning models, random forest model and XGBoost model, were built and trained with the historical transaction data and subsequently tested with separate data. XGBoost model shows higher K-S value, suggesting better classification capability in this task. Top 10 important features from the two models suggest external data such as zhimaScore, multi-platform stacking loans information, and social network information are important factors in predicting loan default probability.
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中文摘要：
在线领先通过更有效的贷款处理打乱了传统的消费银行业。风险预测和监控对于商业模式的成功至关重要。传统的信用评分模型在应用大数据技术构建风险模型方面存在不足。在这份手稿中，从公共网站、第三方收集了各种格式和大小的数据，并与客户的贷款申请信息数据组合在一起。建立集成机器学习模型，随机森林模型和XGBoost模型，并用历史事务数据进行训练，然后用单独的数据进行测试。XGBoost模型显示更高的K-S值，表明该任务具有更好的分类能力。两个模型的前10个重要特征表明，zhimaScore、多平台叠加贷款信息和社交网络信息等外部数据是预测贷款违约概率的重要因素。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Risk Management 风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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