英文标题：
《P2P Loan acceptance and default prediction with Artificial Intelligence》
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作者：
Jeremy D. Turiel and Tomaso Aste
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最新提交年份：
2019
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英文摘要：
Logistic Regression and Support Vector Machine algorithms, together with Linear and Non-Linear Deep Neural Networks, are applied to lending data in order to replicate lender acceptance of loans and predict the likelihood of default of issued loans. A two phase model is proposed; the first phase predicts loan rejection, while the second one predicts default risk for approved loans. Logistic Regression was found to be the best performer for the first phase, with test set recall macro score of $77.4 \\%$. Deep Neural Networks were applied to the second phase only, were they achieved best performance, with validation set recall score of $72 \\%$, for defaults. This shows that AI can improve current credit risk models reducing the default risk of issued loans by as much as $70 \\%$. The models were also applied to loans taken for small businesses alone. The first phase of the model performs significantly better when trained on the whole dataset. Instead, the second phase performs significantly better when trained on the small business subset. This suggests a potential discrepancy between how these loans are screened and how they should be analysed in terms of default prediction.
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中文摘要：
将Logistic回归和支持向量机算法以及线性和非线性深层神经网络应用于贷款数据，以复制贷款人接受贷款的情况，并预测已发放贷款违约的可能性。提出了一种两相流模型；第一阶段预测贷款被拒绝，而第二阶段预测已批准贷款的违约风险。Logistic回归在第一阶段表现最好，测试集回忆宏观得分为77.4 \\%$。深度神经网络仅应用于第二阶段，它们是否达到了最佳性能，对于默认值，验证集召回分数为72 \\%$。这表明AI可以改进当前的信用风险模型，将已发行贷款的违约风险降低70美元。这些模型也适用于仅为小企业提供的贷款。当在整个数据集上进行训练时，模型的第一阶段表现明显更好。相反，第二阶段在接受小企业子集培训时表现明显更好。这表明这些贷款的筛选方式与违约预测分析方式之间存在潜在差异。
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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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一级分类：Quantitative Finance 数量金融学
二级分类：General Finance 一般财务
分类描述：Development of general quantitative methodologies with applications in finance
通用定量方法的发展及其在金融中的应用
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