Policy-Aware Enterprise Risk Assessment through LLM-Based Cross-Source Representation and Reasoning

Abstract

Against the backdrop of frequent policy adjustments and continuous changes in the business environment, traditional enterprise risk identification methods generally suffer from insufficient perception of external institutional constraints, limited ability to integrate multi-source heterogeneous information, and weak semantic interpretability in the risk judgment process. These shortcomings make it difficult to meet the practical needs of dynamic identification and comprehensive assessment of enterprise risks in complex governance scenarios. To address these issues, this paper proposes an LLM Agent-based enterprise risk judgment method that integrates policy text analysis and enterprise operational status modeling. This method incorporates regulatory requirements, constraint logic, and objects of action from policy texts with the enterprise's financial performance, operational status, and abnormal signals into a unified analytical framework, enabling cross-source semantic modeling and intelligent judgment of enterprise risks. Methodologically, the policy text is first structurally analyzed to extract institutional constraint information and regulatory semantic clues related to enterprise risks. Then, the enterprise's operational status is dynamically represented, constructing a multi-dimensional state representation that reflects solvency, profitability, cash flow pressure, and operational stability. Based on this, a cross-source alignment mechanism is used to map policy semantics and enterprise state information to a unified representation space, and the LLM Agent's memory organization and reasoning decision-making capabilities are combined to complete the risk judgment. This method not only enhances the modeling ability of the correlation between policy information and enterprise operational information but also improves the integrity, consistency, and semantic expressiveness of the risk assessment process. Comparative experimental results show that the proposed method exhibits good overall performance in enterprise risk identification tasks, indicating that the constructed policy text parsing and enterprise operational status joint modeling framework can provide effective support for enterprise risk assessment in complex scenarios.