Due to the powerful learning ability on high-rank and non-linear features, deep neural networks (DNNs) are being applied to data mining and machine learning in various fields, and exhibit higher discrimination performance than conventional methods. However, the applications based on DNNs are rare in enterprise credit rating tasks because most of DNNs employ the "end-to-end" learning paradigm, which outputs the high-rank representations of objects and predictive results without any explanations. Thus, users in the financial industry cannot understand how these high-rank representations are generated, what do they mean and what relations exist with the raw inputs. Then users cannot determine whether the predictions provided by DNNs are reliable, and not trust the predictions providing by such "black box" models. Therefore, in this paper, we propose a novel network to explicitly model the enterprise credit rating problem using DNNs and attention mechanisms. The proposed model realizes explainable enterprise credit ratings. Experimental results obtained on real-world enterprise datasets verify that the proposed approach achieves higher performance than conventional methods, and provides insights into individual rating results and the reliability of model training.

中文翻译：

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通过深度特征交叉网络可解释的企业信用评级

由于对高阶和非线性特征的强大学习能力，深度神经网络（DNN）正被应用于各个领域的数据挖掘和机器学习中，并且表现出比传统方法更高的判别性能。然而，基于 DNN 的应用在企业信用评级任务中很少见，因为大多数 DNN 采用“端到端”学习范式，无需任何解释即可输出对象的高级表示和预测结果。因此，金融行业的用户无法理解这些高级表示是如何生成的，它们的含义是什么以及与原始输入存在什么关系。然后用户无法确定 DNN 提供的预测是否可靠，并且不信任此类“黑匣子”模型提供的预测。所以，在本文中，我们提出了一种新颖的网络，使用 DNN 和注意力机制对企业信用评级问题进行显式建模。所提出的模型实现了可解释的企业信用评级。在真实世界的企业数据集上获得的实验结果证实，所提出的方法比传统方法获得了更高的性能，并提供了对个人评分结果和模型训练可靠性的洞察。