Data Science Approaches to Optimizing Insurance Reserve Management and Financial Stability
Abstract
Effective insurance reserve management is crucial for maintaining financial stability in the insurance industry. This study explores various data science approaches to optimize reserve management, including machine learning models, statistical methods, big data analytics, and optimization algorithms. By evaluating techniques such as Gradient Boosting Machines for reserve estimation, statistical methods for forecasting, and big data analytics for risk assessment, the study demonstrates significant improvements in accuracy and efficiency. Results indicate that advanced machine learning models and data analytics enhance predictive accuracy and risk management, while optimization algorithms improve reserve allocation strategies. These insights offer a robust framework for insurers to achieve better financial stability and decision-makin.