# Adversarial Semi-supervised Learning for Corporate Credit Ratings

arXiv: https://arxiv.org/abs/2104.02479 · HTML: https://mlsanity.com/paper/2104.02479/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 4 Apr 2021.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](https://mlsanity.com/paper/1606.03657/) (2016)
- [Adversarial Self-Supervised Learning for Semi-Supervised 3D Action Recognition](https://mlsanity.com/paper/2007.05934/) (2020)
- [DeepTrax: Embedding Graphs of Financial Transactions](https://mlsanity.com/paper/1907.07225/) (2019)
- [Application of Deep Neural Networks to assess corporate Credit Rating](https://mlsanity.com/paper/2003.02334/) (2020)
- [Every Corporation Owns Its Structure: Corporate Credit Ratings via Graph Neural Networks](https://mlsanity.com/paper/2012.01933/) (2020)
- [Is Image Encoding Beneficial for Deep Learning in Finance? An Analysis of Image Encoding Methods for the Application of Convolutional Neural Networks in Finance](https://mlsanity.com/paper/2010.08698/) (2020)
- [Every Corporation Owns Its Image: Corporate Credit Ratings via Convolutional Neural Networks](https://mlsanity.com/paper/2012.03744/) (2020)
- [Contrastive Pre-training for Imbalanced Corporate Credit Ratings](https://mlsanity.com/paper/2102.12580/) (2021)

## Built on by (most cited first, 2 of 2)

- [Corporate Credit Rating: A Survey](https://mlsanity.com/paper/2309.14349/) (2023)
- [CreditARF: A Framework for Corporate Credit Rating with Annual Report and Financial Feature Integration](https://mlsanity.com/paper/2508.02738/) (2025)

## Abstract (arXiv)

Corporate credit rating is an analysis of credit risks within a corporation, which plays a vital role during the management of financial risk. Traditionally, the rating assessment process based on the historical profile of corporation is usually expensive and complicated, which often takes months. Therefore, most of the corporations, which are lacking in money and time, can't get their own credit level. However, we believe that although these corporations haven't their credit rating levels (unlabeled data), this big data contains useful knowledge to improve credit system. In this work, its major challenge lies in how to effectively learn the knowledge from unlabeled data and help improve the performance of the credit rating system. Specifically, we consider the problem of adversarial semi-supervised learning (ASSL) for corporate credit rating which has been rarely researched before. A novel framework adversarial semi-supervised learning for corporate credit rating (ASSL4CCR) which includes two phases is proposed to address these problems. In the first phase, we train a normal rating system via a normal machine-learning algorithm to give unlabeled data pseudo rating level. Then in the second phase, adversarial semi-supervised learning is applied uniting labeled data and pseudo-labeled data. To demonstrate the effectiveness of the proposed ASSL4CCR, we conduct extensive experiments on the Chinese public-listed corporate rating dataset, which proves that ASSL4CCR outperforms the state-of-the-art methods consistently.