CreditARF: A Framework for Corporate Credit Rating with Annual Report and Financial Feature Integration
This addresses the problem of overlooking non-financial data in credit rating for financial analysts and institutions, though it is incremental as it builds on existing deep learning methods.
The paper tackled corporate credit rating by integrating financial data with features from annual reports using FinBERT, resulting in an 8-12% improvement in prediction accuracy.
Corporate credit rating serves as a crucial intermediary service in the market economy, playing a key role in maintaining economic order. Existing credit rating models rely on financial metrics and deep learning. However, they often overlook insights from non-financial data, such as corporate annual reports. To address this, this paper introduces a corporate credit rating framework that integrates financial data with features extracted from annual reports using FinBERT, aiming to fully leverage the potential value of unstructured text data. In addition, we have developed a large-scale dataset, the Comprehensive Corporate Rating Dataset (CCRD), which combines both traditional financial data and textual data from annual reports. The experimental results show that the proposed method improves the accuracy of the rating predictions by 8-12%, significantly improving the effectiveness and reliability of corporate credit ratings.