Soft reordering one-dimensional convolutional neural network for credit scoring

• Published in: Knowledge-based systems Vol. 266; p. 110414
• Main Authors: Qian, Hongyi, Ma, Ping, Gao, Songfeng, Song, You
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 22.04.2023
• Subjects: Convolutional neural networks; Credit scoring; Deep learning; Machine learning; Soft reordering mechanism; Deep learning; Convolutional neural networks; Credit scoring; Machine learning; Soft reordering mechanism
• ISSN: 0950-7051; 1872-7409
• DOI: 10.1016/j.knosys.2023.110414

Credit scoring systems have seen revolutionary development in recent decades, with many classification algorithms being proposed. However, with the increase in the data volume, the performance of traditional algorithms tends to encounter bottlenecks. Although deep learning methods have advantages in handling big data, they are not commonly applied in credit scoring. As one of the most frequently used methods in deep learning, convolutional neural network (CNN) use convolutional kernels as feature extraction tools and has been very successful in tasks related to images or text. This is because image and text data naturally have a structural characteristic called spatial local correlation, which means that the pixels or tokens covered by the same convolutional kernel are highly correlated, and they can be jointly processed to extract meaningful feature representations. However, the tabular data used for credit scoring do not naturally have such a characteristic. The main contribution of this paper is to propose a novel end-to-end soft reordering one-dimensional CNN (SR-1D-CNN), which can adaptively reorganize the original tabular data and make them more conducive to CNN learning. Several real-world credit scoring datasets of different sizes are used for a comprehensive comparison with traditional machine learning classifiers and other deep learning methods. The experimental results demonstrate that the soft reordering mechanism can effectively improve the classification effect of the CNN for tabular data. With the increase in the data scale, the proposed approach obtains superior results to those of other benchmark models.