Intelligent Corporate Risk Management through Adaptive Graph Convolution Networks

Abstract

Corporate risk management has become increasingly complex due to dynamic financial environments, interconnected business structures, and rapidly changing market conditions. Traditional risk assessment models fail to capture interdependencies between corporate entities and external market factors. This study proposes an Intelligent Corporate Risk Management framework using Adaptive Graph Convolution Networks (ACGN-Risk). The model represents corporate entities as nodes in a dynamic graph, where edges capture financial, operational, and market dependency relationships. An adaptive graph convolution mechanism is introduced to dynamically update risk propagation across the corporate network. The proposed framework is evaluated using multi-dimensional corporate risk indicators such as financial instability, credit exposure, market volatility, and operational risk. Experimental results demonstrate that the adaptive graph-based model significantly improves risk prediction accuracy and early warning capability compared to traditional statistical and machine learning approaches.