Enterprise Compliance Risk Identification and Economic Management Optimization Based on Dynamic Bayesian Network

With the rapid development of enterprise digitalization and the global economic environment, enterprise compliance risks and economic management issues have become increasingly complex. Accurate risk identification and efficient economic decision‐making are crucial for the sustainable development of enterprises. To address this challenge, this study proposes an enterprise compliance risk identification and economic management optimization model that combines Dynamic Bayesian Network (DBN) with Artificial Intelligence (AI)‐driven Reinforcement Learning (RL). The study utilizes a financial risk insight dataset and begins with data preprocessing, including missing value imputation, feature standardization, and time series alignment. Subsequently, a DBN model is constructed, with risk variable conditional probability distributions learned via the Markov Chain Monte Carlo (MCMC) method. An AI‐driven RL algorithm is integrated to optimize network parameters, enabling the capture of dynamic evolution and dependencies among risk factors. Empirical results indicate that: (1) The proposed model achieves an AUC‐ROC of 0.981 in risk identification tasks, representing an 8.9% improvement over the best baseline model, with an F1‐score of 0.937. (2) AI‐assisted auditing significantly enhances operational efficiency: the average working hours per case are reduced by 15.3%, the detection rate of high‐risk cases is increased by 15.8%, and customer satisfaction is raised to 4.76 points (out of 5). (3) In terms of economic management optimization, the RL strategy increases the risk‐adjusted return on capital (RAROC) to 0.236, maintains it at 0.187 under crisis scenarios, and achieves a compliance cost savings ratio of 0.237. These results verify the effectiveness of the DBN‐DRL collaborative framework in balancing risk control and economic benefits. This study provides a data‐driven intelligent tool enabling dynamic risk identification and economic decision optimization, offering theoretical foundations and practical references for enterprises to construct efficient and sustainable compliance risk management systems.