AI can enhance actuarial processes by introducing new algorithms for competitive premiums, implementing real-time pricing engines for faster deployment, and integrating predictive analytics with existing systems. This allows insurers to adapt more quickly to market changes, optimize their portfolios, and improve decision-making, ultimately leading to better pricing strategies and reduced operational costs.

Insurers using SAS Dynamic Actuarial Modeling have reported several benefits, including a 15% reduction in data quality issues, a 20% decrease in time needed to integrate external data sources, and a 95% reduction in time to deploy new insurance rates. Additionally, they experience a 50% decrease in premium modeling costs and improved retention levels for key segments.

Insurers face challenges such as the need to adapt quickly to market demands, which can take months for rate approvals, and the necessity to remain relevant with insightful pricing models. They also need to improve operational efficiency to support agility and protect their customer base amidst changing attitudes towards loyalty and the ease of comparing prices online.