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📐 '''Relativity''' is a multiplicative factor used in [[Definition:Insurance rating | insurance rating]] that expresses how much more or less risk a particular class, territory, or characteristic carries compared to a chosen base level. In [[Definition:Personal lines | personal lines]] [[Definition:Auto insurance | auto insurance]], for example, a young driver class might have a relativity of 1.45, meaning the expected [[Definition:Loss cost | loss cost]] for that group is 45 percent higher than the base class, while a middle-aged driver segment might sit at 0.85. These factors are the building blocks of a [[Definition:Rating algorithm | rating algorithm]] and, when chained together across multiple variables, produce the final [[Definition:Premium | premium]] that reflects each insured's unique risk profile.

🔧 [[Definition:Actuary | Actuaries]] derive relativities through statistical analysis of historical [[Definition:Loss experience | loss experience]], employing techniques such as [[Definition:Generalized linear model (GLM) | generalized linear models]], minimum-bias procedures, or increasingly, [[Definition:Machine learning | machine learning]] approaches that can detect complex interactions among rating variables. Once calculated, the relativities feed into a [[Definition:Rate filing | rate filing]] submitted to the relevant [[Definition:Insurance regulator | state regulator]], who evaluates whether the proposed differentials are [[Definition:Actuarial justification | actuarially justified]] and not [[Definition:Unfair discrimination | unfairly discriminatory]]. A well-constructed set of relativities ensures that each [[Definition:Risk class | risk class]] pays a premium proportional to its expected cost, preventing [[Definition:Cross-subsidization | cross-subsidization]] that could drive profitable segments to competitors.

💡 Getting relativities right is one of the most consequential exercises in an insurer's pricing function. If a territory relativity underestimates [[Definition:Catastrophe risk | catastrophe exposure]] in a coastal zone, the company attracts adverse selection and accumulates hidden losses. Conversely, overstating a relativity for a low-risk segment pushes desirable customers toward rivals, eroding the book's overall profitability. Modern [[Definition:Insurtech | insurtech]] platforms have accelerated the speed at which carriers can refresh relativities — incorporating real-time [[Definition:Telematics | telematics]] data, [[Definition:Credit-based insurance score | credit-based scores]], and other granular inputs — making pricing a dynamic, continuously refined discipline rather than a once-a-year exercise.

'''Related concepts'''
{{Div col|colwidth=20em}}
* [[Definition:Rating factor]]
* [[Definition:Loss cost]]
* [[Definition:Generalized linear model (GLM)]]
* [[Definition:Rate filing]]
* [[Definition:Risk classification]]
* [[Definition:Actuarial pricing]]
{{Div col end}}

Definition:Relativity - Revision history

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