CreditARF: A Framework for Corporate Credit Rating with Annual Report and Financial Feature Integration

TL;DR

This paper presents CreditARF, a novel framework that combines financial metrics and unstructured annual report text data using FinBERT to enhance corporate credit rating accuracy.

Contribution

It introduces a new integrated framework and a large-scale dataset that leverage both financial and textual data for improved credit rating predictions.

Findings

Rating accuracy improved by 8-12%

Developed the comprehensive CCRD dataset

Enhanced reliability of credit ratings

Abstract

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.