表格图像：一种将表格数据转换为图像以用于卷积神经网络的方法
Tabular Image: a method to convert tabular data to images for convolutional neural networks
AI总结：
本研究提出将表格数据转换为图像的Tabular Image方法，利用二维卷积神经网络提升信用评分性能，在基准数据集上达到顶尖表现且在大型数据集优势更明显，还提供适配多领域表格数据的灵活框架。
AI中文摘要：
提高信用评分模型的预测能力始终是金融领域的研究热点。鉴于神经网络在计算机视觉、自然语言处理等不同领域展现出的卓越有效性，各类神经网络已被测试用于潜在提升信用数据下的贷款违约预测。然而，由于信用数据主要为表格型，其结构与特性并不适配神经网络，阻碍了神经网络在信用评分中超越传统机器学习模型，成为显著挑战。为克服该挑战，我们提出一种名为“表格图像（Tabular Image）”的新型数据转换方法，将表格数据转换为图像，以利用在图像上表现极佳的强大二维卷积神经网络，同时缓解表格数据给深度网络带来的挑战。与现有转换方法相比，表格图像通过在图像中创造性嵌入信用评分的两个关键指标——证据权重（Weight of Evidence）和信息价值（Information Value），可将表格数据转换为紧凑且鲁棒的图像。在三个信用评分基准数据集上的应用表明，仅使用表格图像训练二维卷积神经网络即可达到顶尖的预测性能；此外，该方法的预测优势在大型数据集中更为显著。我们的创新方法为通过合适的数据表示方式在信用评分中利用二维卷积神经网络提供了可能，同时提供了灵活框架以适配其他领域的各类表格数据集。
英文摘要：
Improving the predictive capability of credit scoring models is always an active area of research in the financial sector. Recognising the impressive effectiveness of neural networks in different domains (such as computer vision and natural language processing), various neural networks have been tested to potentially improve loan default prediction on credit data. Nevertheless, a significant challenge emerges due to the predominantly tabular nature of credit data, which is not well-suited to the structure and strengths of neural networks, hindering their ability to surpass traditional machine learning models in credit scoring. To overcome the challenge, we propose a novel data transformation method called \textit{Tabular Image} that converts tabular data into images to take advantage of the powerful two-dimensional convolutional neural networks that perform extremely well on images while mitigating the challenges tabular data poses to deep networks. The \textit{Tabular Image} can convert tabular data into compact and resilient images compared with existing transformation methods by creatively embedding two crucial measures in credit scoring, the weight of evidence and information value, in the image. Applications to three credit scoring benchmark datasets suggest that simply training a two-dimensional convolutional neural network with \textit{Tabular Image} can provide state-of-the-art predictive performance. In addition, the advantage of our proposed method's prediction is more evident in the large dataset. Our innovative approach raises the possibility of leveraging two-dimensional convolutional neural networks in credit scoring using a proper data representation method. Furthermore, a flexible framework is provided to suit various tabular datasets in other domains.
