ML ‐ SGNN : A Novel Similarity‐Based Graph Neural Network With Meta‐Learning for Credit Classification With Low Sample Size

Abstract

In the field of credit risk classification, the prediction model needs to meet a large number of labelled samples to make the model fully trained. However, in some practical scenarios, it is difficult for financial institutions to obtain a large number of available labelled samples because of the cost or privacy. Shortage of samples will cause the prediction model ineffective or even invalid. To address low sample size problem, a novel similarity‐based graph neural network with meta‐learning called ML‐SGNN is proposed for credit classification with low sample size. In the proposed ML‐SGNN methodology, attribute co‐training based on meta‐learning strategy is first introduced to increase the generalisation capability of the proposed model under label scarcity condition. Then, a node update strategy based on similarity is proposed to aggregate features of samples with same label so as to provide a contrastive signal that increases the discriminative information extracted from low sample size. Finally, a novel similarity‐based edge update strategy utilising the difference of node features and convolutional neural network is proposed to better describe the similarity relationships among unordered attribute structures to improve the generalizability of ML‐SGNN. The experiment results demonstrate that the proposed ML‐SGNN method consistently achieves the highest average ranking across different sample sizes and evaluation metrics among the compared methods. Generalisation capability of ML‐SGNN indicating that ML‐SGNN can be considered as a promising solution for credit classification with low sample size.