Enterprise violation risk deduction combining generative AI and event evolution graph

Abstract

In the current realms of scientific research and commercial applications, the risk inference of regulatory violations by publicly listed enterprises has attracted considerable attention. However, there are some problems in the existing research on the deduction and prediction of violation risk of listed enterprises, such as the lack of analysis of the causal logic association between violation events, the low interpretability and effectiveness of the deduction and the lack of training data. To solve these problems, we propose a framework for enterprise violation risk deduction based on generative AI and event evolution graphs. First, the generative AI technology was used to generate a new text summary of the lengthy and complex enterprise violation announcement to realize a concise overview of the violation matters. Second, by fine‐tuning the generative AI model, an event entity and causality extraction framework based on automated data augmentation are proposed, and the UIE (Unified Structure Generation for Universal Information Extraction) event entity extraction model is used to create the event entity extraction for listed enterprises ‘violations. Then, a causality extraction model CDDP‐GAT (Event Causality Extraction Based on Chinese Dictionary and Dependency Parsing of GAT) is proposed. This model aims to identify and analyse the causal links between corporate breaches, thereby deepening the understanding of the event logic. Then, the merger of similar events was realized, and the causal correlation weights between enterprise violation‐related events were evaluated. Finally, the listed enterprise's violation risk event evolution graph was constructed, and the enterprise violation risk deduction was carried out to form an expert system of financial violations. The deduction results show that the method can effectively reveal signs of enterprise violations and adverse consequences.