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.

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