Contrastive Pre-training for Imbalanced Corporate Credit Ratings
This work addresses the challenge of long-tail distributions in corporate credit rating systems, which is crucial for financial risk control, though it appears incremental as it adapts existing self-supervised techniques to a specific domain.
The paper tackles the class imbalance problem in corporate credit rating data by proposing a contrastive pre-training framework (CP4CCR) that uses self-supervised tasks to learn a better initialization, which improves the performance of standard rating models, particularly for classes with few samples, as demonstrated on a Chinese public-listed corporate rating dataset.
Corporate credit rating reflects the level of corporate credit and plays a crucial role in modern financial risk control. But real-world credit rating data usually shows long-tail distributions, which means heavy class imbalanced problem challenging the corporate credit rating system greatly. To tackle that, inspried by the recent advances of pre-train techniques in self-supervised representation learning, we propose a novel framework named Contrastive Pre-training for Corporate Credit Rating (CP4CCR), which utilizes the self-surpervision for getting over class imbalance. Specifically, we propose to, in the first phase, exert constrastive self-superivised pre-training without label information, which want to learn a better class-agnostic initialization. During this phase, two self-supervised task are developed within CP4CCR: (i) Feature Masking (FM) and (ii) Feature Swapping(FS). In the second phase, we can train any standard corporate redit rating model initialized by the pre-trained network. Extensive experiments conducted on the Chinese public-listed corporate rating dataset, prove that CP4CCR can improve the performance of standard corporate credit rating models, especially for class with few samples.