Historical data allows insurers to identify long-term trends, detect anomalies, and refine risk segmentation based on claim frequency and severity patterns. By incorporating machine learning, insurers can dynamically adjust premiums to reflect emerging risks and market shifts, reducing underpricing and mitigating adverse selection. McKinsey reported a 15-20% increase in pricing efficiency when companies combine historical data with external factors (e.g., economic indicators, natural disaster forecasts) in risk forecasting.