用图像+CNN分析中小企业破产风险，准确率达97.8%
Bankruptcy analysis using images and convolutional neural networks (CNN)
- 将企业财务数据转为图像输入CNN进行破产预测
- 使用超1万张图像训练，准确率高达97.8%
- 为中小企业风险评估提供新方法，适合金融风控领域
金融机构营销部门常向大型企业倾斜资源，因其风险更易评估。传统风险研究多集中于上市企业，忽视了中小型企业（SMEs）的评估需求。本研究提出一种新方法：将每家企业的财务数据转化为图像，输入卷积神经网络（CNN）进行分析。研究生成超过10,000张图像，用于训练模型，结果表明在大量图像支持下，模型具备显著预测能力，准确率达97.8%。该图像生成方法具有跨行业应用潜力，可拓展至多种分析场景。
原文摘要 · Abstract (English)
The marketing departments of financial institutions strive to craft products and services that cater to the diverse needs of businesses of all sizes. However, it is evident upon analysis that larger corporations often receive a more substantial portion of available funds. This disparity arises from the relative ease of assessing the risk of default and bankruptcy in these more prominent companies. Historically, risk analysis studies have focused on data from publicly traded or stock exchange-listed companies, leaving a gap in knowledge about small and medium-sized enterprises (SMEs). Addressing this gap, this study introduces a method for evaluating SMEs by generating images for processing via a convolutional neural network (CNN). To this end, more than 10,000 images, one for each company in the sample, were created to identify scenarios in which the CNN can operate with higher assertiveness and reduced training error probability. The findings demonstrate a significant predictive capacity, achieving 97.8% accuracy, when a substantial number of images are utilized. Moreover, the image creation method paves the way for potential applications of this technique in various sectors and for different analytical purposes.
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