英文标题：
《Neural Learning of Online Consumer Credit Risk》
---
作者：
Di Wang, Qi Wu, Wen Zhang
---
最新提交年份：
2019
---
英文摘要：
  This paper takes a deep learning approach to understand consumer credit risk when e-commerce platforms issue unsecured credit to finance customers\' purchase. The \"NeuCredit\" model can capture both serial dependences in multi-dimensional time series data when event frequencies in each dimension differ. It also captures nonlinear cross-sectional interactions among different time-evolving features. Also, the predicted default probability is designed to be interpretable such that risks can be decomposed into three components: the subjective risk indicating the consumers\' willingness to repay, the objective risk indicating their ability to repay, and the behavioral risk indicating consumers\' behavioral differences. Using a unique dataset from one of the largest global e-commerce platforms, we show that the inclusion of shopping behavioral data, besides conventional payment records, requires a deep learning approach to extract the information content of these data, which turns out significantly enhancing forecasting performance than the traditional machine learning methods.
---
中文摘要：
本文采用深入学习的方法来理解电子商务平台发行无担保信贷为客户购买融资时的消费信贷风险。当每个维度的事件频率不同时，“NeuCredit”模型可以捕获多维时间序列数据中的两个序列相关性。它还捕获了不同时间演化特征之间的非线性横截面相互作用。此外，预测违约概率的设计应具有可解释性，以便将风险分解为三个组成部分：表示消费者还款意愿的主观风险、表示其还款能力的客观风险以及表示消费者行为差异的行为风险。使用来自全球最大电子商务平台之一的独特数据集，我们表明，除了传统的支付记录之外，还需要使用深度学习方法来提取这些数据的信息内容，这大大提高了预测性能，而不是传统的机械学习方法。
---
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
--
一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
--

---
PDF下载：
-->