Binomial distribution is the probability model used when an outcome has two possible states, such as claim/no-claim in a set of policy units.
Why insurers use it
In underwriting and rate review, actuaries use binomial logic to estimate the expected number of claims in a fixed exposure group when each exposure has an estimated claim probability.
Claims and pricing implications
If actual frequency deviates from the expected binomial outcome, pricing assumptions are reviewed. Higher observed frequency can increase reserve requirements and future rates, while lower frequency can support refinements or competitive pricing moves.
Practical use in insurance
The model is commonly used in line-level frequency analysis and scenario testing, especially in lines with many small homogeneous exposures.
Practical example
If a insurer writes 1,000 small home policies with a 2% expected claim probability over a year, binomial assumptions help estimate how many claims to expect and how much capital should be held.