A new representation learning approach for credit data analysis

Representation learning has an important impact on the performance of machine learning methods and has been used to solve many distribution problems for numerous graphical and sequential mining tasks. While the distributions of credit data are very complex, the representations of such data are less studied. This study proposes a new representation learning approach based on a neural network called NyströmNet, which represents the credit data to benefit credit evaluation and sub-pattern analysis. The NyströmNet is developed to utilize the advantages of the Nyström method – a kernel approximation method in credit evaluation, yet overcomes its two limitations: distance distortions in kernel functions, and parameter tuning. The two main modules contained in NyströmNet, i.e., the Distance Metric Learning module and the Nyström module, can benefit each other and yield an overall optimum. Experiments using six real-life large-scale credit data showed that the AUC of the distance-based classifiers and the linear classifiers were improved by 2–11% and 2–14% with the newly generated distributions. The proposed approach also has certain practical advantages over traditional approaches because it is free from complex parameter tuning, consumes fewer memories, and is easy to utilize automatic differential frameworks such as PyTorch. The proposed approach is highly suitable for large-scale credit evaluation.