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This approach to estimating the expected cost of claims combines data from a specific risk (e.g., a particular driver, building, or business) with data from a larger, similar group. A smaller risk’s own limited experience might not accurately reflect its true long-term claim costs. Therefore, its experience is given a lower statistical “weight.” The experience of the larger group is given a higher weight, reflecting its greater statistical reliability. These weights are then applied to the respective average claim costs, producing a blended estimate that balances individual risk characteristics with the stability of broader data. For example, a new driver with limited driving history will have their individual experience blended with the experience of a larger pool of similar new drivers to arrive at a more reliable predicted cost.

Balancing individual and group data leads to more stable and accurate ratemaking. This protects insurers from underpricing risks due to insufficient individual data and policyholders from unfairly high premiums based on limited experience. This method, developed over time through actuarial science, has become essential for managing risk and maintaining financial stability in the insurance industry. It ensures fairness and predictability in pricing for both insurers and insured parties.

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