Knowing which factors are significant in credit rating assignment leads to better decision-making. However, the focus of the literature thus far has been mostly on structured data, and fewer studies have addressed unstructured or multi-modal datasets. In this paper, we present an analysis of the most effective architectures for the fusion of deep learning models for the prediction of company credit rating classes, by using structured and unstructured datasets of different types. In these models, we tested different combinations of fusion strategies with different deep learning models, including CNN, LSTM, GRU, and BERT. We studied data fusion strategies in terms of level (including early and intermediate fusion) and techniques (including concatenation and cross-attention). Our results show that a CNN-based multi-modal model with two fusion strategies outperformed other multi-modal techniques. In addition, by comparing simple architectures with more complex ones, we found that more sophisticated deep learning models do not necessarily produce the highest performance; however, if attention-based models are producing the best results, cross-attention is necessary as a fusion strategy. Finally, our comparison of rating agencies on short-, medium-, and long-term performance shows that Moody's credit ratings outperform those of other agencies like Standard & Poor's and Fitch Ratings.

翻译：了解信用评级分配中的重要因素有助于做出更优决策。然而，现有文献主要关注结构化数据，鲜有研究涉及非结构化或多模态数据集。本文通过使用不同类型的结构化与非结构化数据集，分析了预测公司信用评级类别的最优深度学习模型融合架构。在这些模型中，我们测试了不同融合策略与多种深度学习模型（包括CNN、LSTM、GRU及BERT）的组合。我们从融合层级（包含早期融合与中间融合）和技术层面（包含拼接与交叉注意力）对数据融合策略进行了研究。结果表明，采用两种融合策略的基于CNN的多模态模型优于其他多模态技术。此外，通过对比简单架构与复杂架构，我们发现更复杂的深度学习模型并不必然产生最高性能；但当基于注意力的模型取得最佳结果时，交叉注意力作为融合策略是必要的。最后，我们对评级机构在短期、中期和长期表现上的比较表明，穆迪的信用评级优于标准普尔和惠誉等其他机构。