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📊 '''Claims analytics''' refers to the systematic application of data analysis, statistical modeling, and [[Definition:Machine learning | machine learning]] techniques to [[Definition:Insurance claim | insurance claims]] data in order to uncover patterns, predict outcomes, and drive better decision-making across the [[Definition:Claims administration | claims administration]] lifecycle. [[Definition:Insurance carrier | Insurers]], [[Definition:Third-party administrator (TPA) | TPAs]], and [[Definition:Insurtech | insurtechs]] use claims analytics to go beyond simple reporting — transforming raw claims information into actionable intelligence that influences everything from [[Definition:Reserve | reserving]] accuracy to [[Definition:Insurance fraud | fraud]] detection and [[Definition:Litigation management | litigation management]].

⚙️ At its core, claims analytics ingests structured and unstructured data — claim files, adjuster notes, medical records, weather data, [[Definition:Telematics | telematics]] feeds, and more — and applies descriptive, predictive, and prescriptive models to it. Descriptive analytics reveals historical trends such as average [[Definition:Claims development | claim development]] timelines by [[Definition:Line of business | line of business]] or geographic [[Definition:Loss trend | loss trends]]. Predictive models go further: they might flag newly reported claims that have a high probability of becoming [[Definition:Large loss | large losses]], enabling early intervention by senior [[Definition:Claims adjuster | adjusters]] or [[Definition:Special investigation unit (SIU) | special investigation units]]. Prescriptive analytics recommends specific actions — for instance, which claims to settle quickly versus which to litigate based on projected outcomes. These tools often integrate with the carrier's [[Definition:Claims management system | claims management system]] so that insights reach the right person at the right point in the workflow.

💡 The financial stakes behind claims analytics are substantial. Claims and [[Definition:Loss adjustment expense (LAE) | loss adjustment expenses]] typically represent the largest single cost for an insurer, so even marginal improvements in accuracy or efficiency translate into meaningful savings. Carriers that deploy analytics effectively can reduce [[Definition:Claims leakage | claims leakage]] — payments that exceed what the claim truly warrants — tighten [[Definition:Reserve adequacy | reserve adequacy]], and shorten cycle times. Beyond cost control, analytics also feeds back into [[Definition:Underwriting | underwriting]] and [[Definition:Product development | product design]]: understanding which claims are most frequent, most severe, and most prone to disputes allows the organization to refine [[Definition:Rating | rating]] models and [[Definition:Policy language | policy language]], closing the loop between what the insurer promises and what it ultimately pays.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Predictive modeling]]
* [[Definition:Claims leakage]]
* [[Definition:Special investigation unit (SIU)]]
* [[Definition:Claims management system]]
* [[Definition:Loss triangle]]
* [[Definition:Artificial intelligence (AI)]]
{{Div col end}}

Definition:Claims analytics - Revision history

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