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首页> 外文会议>IEEE International Conference on Computer and Communications >Every Corporation Owns Its Image: Corporate Credit Ratings via Convolutional Neural Networks
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Every Corporation Owns Its Image: Corporate Credit Ratings via Convolutional Neural Networks

机译：每个公司都拥有它的形象：公司信用评级通过卷积神经网络

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Credit rating is an analysis of the credit risks associated with a corporation, which reflect the level of the riskiness and reliability in investing. There have emerged many studies that implement machine learning techniques to deal with corporate credit rating. However, the ability of these models is limited by enormous amounts of data from financial statement reports. In this work, we analyze the performance of traditional machine learning models in predicting corporate credit rating. For utilizing the powerful convolutional neural networks and enormous financial data, we propose a novel end-to-end method, Corporate Credit Ratings via Convolutional Neural Networks, CCR-CNN for brevity. In the proposed model, each corporation is transformed into an image. Based on this image, CNN can capture complex feature interactions of data, which are difficult to be revealed by previous machine learning models. Extensive experiments conducted on the Chinese public-listed corporate rating dataset which we build, prove that CCR-CNN outperforms the state-of-the-art methods consistently.
机译：信用评级是对与公司相关的信贷风险分析，这反映了投资风险和可靠性水平。出现了许多研究，实现了处理企业信用评级的机器学习技术。但是，这些模型的能力受到财务报表报告中的巨大数据的限制。在这项工作中，我们分析了传统机器学习模型在预测企业信用评级方面的性能。为了利用强大的卷积神经网络和巨大的财务数据，我们提出了一种新的端到端方法，通过卷积神经网络，CCR-CNN用于简洁起来的新的端到端方法。在所提出的模型中，每个公司都转变为图像。基于此图像，CNN可以捕获数据的复杂特征交互，这很难被先前的机器学习模型透露。在中国公开上市的企业评级数据集进行广泛的实验，我们构建，证明了CCR-CNN始终如一地优于最先进的方法。

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