Development of Operational Risk Warning of Fixed Assets Based on Improved Machine Learning Model

Operational risk evaluation of fixed assets is important in assuring the sustainability, efficacy, and financial stability of organizations. Fixed assets like industrial equipment, infrastructure, and long-term equipment need constant surveillance to avoid performance deterioration, sudden breakdown, and regulatory infractions. Historically, risk monitoring relied on rule-based systems, which are labor intensive, error-prone, and tend to be reactive more than proactive. To counter these constraints Decision Trees model have been developed to automate and enhance risk warning processes. Although these models provide advantages such as automation and pattern detection, they often have problems with imbalanced datasets, lack of dynamic nature, poor interpretability, and decreased precision when subjected to realworld complexities and noise. These drawbacks limit the reliability and scalability of existing operational risk assessment systems. In this research, an Improved Machine Learning Model is introduced to further improve the fixed asset operational risk warning system. The enhanced model utilizes refined preprocessing methods, smarter feature selection, and ensemble learning processes to enhance accuracy, stability, and real-time risk detection. Proposed results demonstrate that the proposed model achieves a classification accuracy of 97.1 %, a precision of 97 %, and an F 1 -score of 96.2 %. The system overcomes current limitations by reducing spurious alarms, better managing data abnormalities, and providing dynamic adaptation in response to changing asset conditions. The suggested method eventually guarantees early failure detection, maximizes asset utilization, and reinforces preventive maintenance techniques.