https://www.insurerbrain.com/w/index.php?action=history&feed=atom&title=Definition%3ARating_algorithm 2026-04-29T14:22:41Z Revision history for this page on the wiki MediaWiki 1.43.8 https://www.insurerbrain.com/w/index.php?title=Definition:Rating_algorithm&diff=7066&oldid=prev 2026-03-10T05:08:41Z

<p>Bot: Creating new article from JSON</p> <p><b>New page</b></p><div>🤖 &#039;&#039;&#039;Rating algorithm&#039;&#039;&#039; is a mathematical model or set of computational rules that an [[Definition:Insurance carrier | insurer]] uses to translate risk characteristics into a [[Definition:Insurance premium | premium]] price for a given [[Definition:Insurance policy | policy]]. Unlike a simple rate table, a rating algorithm may incorporate multivariate analysis, [[Definition:Predictive analytics | predictive analytics]], or [[Definition:Machine learning | machine learning]] techniques to capture complex interactions among [[Definition:Rating factor | rating factors]] — producing more granular and risk-responsive pricing than traditional methods allow.<br /> <br /> ⚙️ These algorithms ingest data from [[Definition:Application | applications]], public records, [[Definition:Credit score | credit-based]] scores, [[Definition:Telematics | telematics]] devices, and other sources, then apply a sequence of calculations that might include generalized linear models (GLMs), gradient-boosted trees, or neural networks. The output feeds into the [[Definition:Rating engine | rating engine]], which delivers a quoted premium to the [[Definition:Underwriter | underwriter]] or directly to the consumer through a digital portal. Developing and calibrating a rating algorithm requires close collaboration between [[Definition:Actuary | actuaries]], data scientists, and [[Definition:Underwriting | underwriting]] leadership, and the resulting model must be validated against historical [[Definition:Loss experience | loss experience]] to confirm its predictive power.<br /> <br /> 🔍 Regulators are paying increasing attention to rating algorithms, particularly when [[Definition:Artificial intelligence (AI) | AI]] or opaque modeling techniques are involved. Concerns center on [[Definition:Unfair discrimination | unfair discrimination]] — whether an algorithm inadvertently uses proxies for protected classes — and on transparency, since [[Definition:State insurance department | state regulators]] may require carriers to explain how a given factor influences price in a [[Definition:Rate filing | rate filing]]. For [[Definition:Insurtech | insurtechs]] building next-generation pricing capabilities, navigating these regulatory expectations while pushing the frontier of algorithmic sophistication is one of the defining challenges of modern [[Definition:Rate regulation | rate regulation]] compliance.<br /> <br /> &#039;&#039;&#039;Related concepts&#039;&#039;&#039;<br /> {{Div col|colwidth=20em}}<br /> * [[Definition:Rating engine]]<br /> * [[Definition:Rating factor]]<br /> * [[Definition:Predictive analytics]]<br /> * [[Definition:Machine learning]]<br /> * [[Definition:Rate filing]]<br /> * [[Definition:Actuarial analysis]]<br /> {{Div col end}}</div>

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