Leveraging AI & MI for Predictive Analytics in Stress Level of Private Insurance Employees : Comprehensive Review

This comprehensive study delves into the revolutionary possibilities of AI and ML in predictive analytics for gauging private insurance industry employees' stress levels. Recognizing the root causes and anticipating stress patterns becomes critical as the industry faces increasing workplace stress. The use of AI and ML allows us to explore several aspects of stress, including stress in the workplace and personal factors that affect stress levels. Starting with an overview of the present state of affairs in the private insurance sector, we underscore the critical nature of resolving this matter immediately. Stress event forecasting, pattern recognition in historical data, and early indication identification are areas where AI and ML are finding practical use. In order to reduce the negative effects of stress, AI-driven algorithms can quickly determine which employees are at high risk and then intervene proactively. Additionally, this article discusses possible biases in AI systems, data privacy, and ethical concerns. The substantial contribution is found in the exhaustive analysis of the revolutionary role of AI and ML in changing the insurance industry, namely in determining which employees pose a high risk and in developing specific interventions to improve their health. Industry experts, lawmakers, and researchers will find this research paper useful since it sheds light on the complex nature of stress and provides concrete suggestions for creating more positive workplaces.