Research on Enterprise Financial Information Risk Identification and Dynamic Control Management Model Based on Genetic Algorithm

In the contemporary global economic landscape, the imperative for enterprise financial informatization as a catalyst for efficiency, cost reduction, and innovation is evident. However, the deepening integration of informatization introduces corresponding risks, necessitating the establishment of effective risk management systems. This paper proposes a genetic algorithm-based model for enterprise financial informatization risk management, aiming to offer precise and actionable solutions. The study integrates genetic algorithms to enhance the adaptability and flexibility of the risk management model in the dynamic information environment. Through in-depth research on corporate financial informatization risks, a multi-dimensional risk management control model is constructed, considering technical, organizational, and environmental factors. The genetic algorithm introduces a new perspective for model optimization, enabling efficient search and optimization capabilities. The model not only identifies but also controls and governs risks, providing timely intervention and effective risk management. Moreover, the genetic algorithm facilitates intelligent decision support for risk management by adapting to changing environments. Empirical analysis validates the model’s feasibility and effectiveness in real enterprise cases, emphasizing its practicality.