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🗂️ '''Classification rating''' is the practice of grouping insurable [[Definition:Exposure | exposures]] into defined categories and assigning [[Definition:Rate | rates]] that reflect the expected loss characteristics of each category. In insurance, it serves as the structural framework that connects a [[Definition:Policyholder | policyholder's]] business type, occupation, or risk profile to a corresponding [[Definition:Premium | premium]] level, functioning as a near-synonym of [[Definition:Class rating | class rating]] though sometimes used more broadly to encompass the entire taxonomy of risk categories an [[Definition:Insurance carrier | insurer]] maintains.

⚙️ Building a sound classification rating system starts with segmenting a population of risks into groups that are internally homogeneous and externally distinct in terms of expected [[Definition:Loss cost | loss costs]]. [[Definition:Actuarial analysis | Actuaries]] analyze years of [[Definition:Claims experience | claims data]], testing whether proposed groupings produce statistically significant differences in [[Definition:Claims frequency | frequency]] and [[Definition:Claims severity | severity]]. Regulatory bodies such as state [[Definition:Department of insurance | departments of insurance]] review these classifications to ensure they are not [[Definition:Unfair discrimination | unfairly discriminatory]]—that is, every rating distinction must be supported by actuarial evidence rather than arbitrary or prohibited factors. Once approved, the classification scheme is embedded in [[Definition:Rating manual | rating manuals]] and [[Definition:Policy administration system | policy administration systems]], where it drives automated [[Definition:Quoting | quoting]] workflows.

🎯 A well-constructed classification rating system balances two competing goals: granularity enough to price risks fairly and simplicity enough to remain operationally manageable. Too few classifications force low-risk [[Definition:Insured | insureds]] to subsidize high-risk ones, inviting [[Definition:Adverse selection | adverse selection]] as competitors pick off the better accounts. Too many classifications fragment the data underlying each group, weakening [[Definition:Statistical credibility | statistical credibility]] and complicating [[Definition:Underwriting | underwriting]] decisions. Emerging [[Definition:Insurtech | insurtech]] capabilities—particularly [[Definition:Machine learning | machine learning]] models that identify subtle risk differentiators—are pushing the industry toward finer segmentation while raising new questions about transparency and regulatory approval.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Class rating]]
* [[Definition:Class code]]
* [[Definition:Rating manual]]
* [[Definition:Actuarial analysis]]
* [[Definition:Experience rating]]
* [[Definition:Unfair discrimination]]
{{Div col end}}

Definition:Classification rating - Revision history

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