A Graph Neural Network Framework for Governance, Risk, and Compliance Classification and Unified Grc Scoring to Strengthen Enterprise-Level Decision Making

Governance, risk management and compliance (GRC) are imperative in maintaining the integrity of business processes especially relating to the supplier relationship. The traditional GRC models often rely on fragmented data, and they fail to capture the complexity of the supplier networks. The presented paper proposes a Graph Neural Network (GNN) model of combined GRC classification and scoring, whereby the goal is to provide a complete and precise view of supplier risk across an organization. Using the relational structure of supplier networks, the GNN model can discover the dependencies that are important, categorize suppliers based on their governance, risk, and compliance attributes, and produce an integrated GRC score that can combine various data streams. Such a score provides practical data in the decision-making of the executive, thus contributing to the enhancement of risk analysis and strategic rulings. Our experimental findings also reveal that the GNN framework is more effective compared to traditional machine learning tools, and, most importantly, it can also be used in situations where the data is sparse or incomplete, which proves its high strength and scalability in real-world applications. The projected framework will provide decision-makers with a more reliable, and more datadriven system of supplier risk management, hence enabling the making of more informed strategic decision-making at the enterprise level.