Every Corporation Owns Its Image: Corporate Credit Ratings via Convolutional Neural Networks
This work provides an incremental improvement in corporate credit rating prediction for financial institutions and investors by leveraging image-based representations for CNNs.
This paper addresses the challenge of corporate credit rating prediction, which is often limited by the volume of financial statement data. The authors propose CCR-CNN, a novel end-to-end method that transforms corporate financial data into images, enabling convolutional neural networks to capture complex feature interactions. Experiments on a Chinese public-listed corporate rating dataset show that CCR-CNN consistently outperforms state-of-the-art methods.
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.