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🗃️ '''Claims data''' encompasses the structured and unstructured information generated throughout the lifecycle of [[Definition:Insurance claim | insurance claims]] — from first notice of loss through investigation, [[Definition:Loss reserving | reserving]], settlement, and closure. For [[Definition:Insurance carrier | insurers]], [[Definition:Reinsurer | reinsurers]], and [[Definition:Managing general agent (MGA) | MGAs]], this data is a foundational asset: it fuels [[Definition:Actuarial analysis | actuarial analysis]], [[Definition:Rate making | pricing models]], [[Definition:Fraud detection | fraud detection]] algorithms, [[Definition:Underwriting | underwriting]] refinement, and regulatory reporting. In an industry built on the quantification of risk, claims data is the empirical record of how risk actually manifests.

📊 A single claim file can produce dozens of data points: dates of loss and notification, [[Definition:Claimant | claimant]] demographics, cause of loss codes, [[Definition:Loss reserving | reserve]] amounts at successive valuations, [[Definition:Indemnity payment | indemnity]] and [[Definition:Loss adjustment expense (LAE) | expense]] payments, litigation status, [[Definition:Subrogation | subrogation]] recoveries, and adjuster notes. Aggregated across thousands of files, these fields reveal [[Definition:Claim frequency | frequency]] and [[Definition:Claim severity | severity]] trends, [[Definition:Loss development | development patterns]], geographic concentrations, and correlations with policy characteristics. Modern [[Definition:Claims management system | claims platforms]] capture this information digitally at the point of entry, but legacy systems often store critical details in free-text fields or scanned documents, creating data-quality challenges that [[Definition:Insurtech | insurtechs]] increasingly address through [[Definition:Natural language processing (NLP) | natural language processing]] and [[Definition:Optical character recognition (OCR) | optical character recognition]] tools.

💡 The strategic value of claims data extends well beyond the claims department. [[Definition:Underwriting | Underwriters]] use loss history to assess individual account risk and to calibrate portfolio-level appetite. [[Definition:Actuary | Actuaries]] rely on clean, granular claims data for [[Definition:Loss reserving | reserve]] adequacy testing and [[Definition:Experience rating | experience rating]]. [[Definition:Reinsurer | Reinsurers]] increasingly demand detailed bordereaux-level claims data as a condition of treaty renewal, and the ability to deliver it promptly signals operational maturity. Regulatory bodies such as the [[Definition:National Association of Insurance Commissioners (NAIC) | NAIC]] mandate standardized claims data submissions for market monitoring. As [[Definition:Predictive analytics | predictive analytics]] and [[Definition:Machine learning | machine learning]] reshape the industry, organizations with superior claims data infrastructure — accurate, timely, and deeply granular — hold a pronounced competitive advantage.

'''Related concepts'''
{{Div col|colwidth=20em}}
* [[Definition:Bordereaux]]
* [[Definition:Loss development]]
* [[Definition:Predictive analytics]]
* [[Definition:Claims management system]]
* [[Definition:Actuarial analysis]]
* [[Definition:Fraud detection]]
{{Div col end}}

Definition:Claims data - Revision history

PlumBot: Bot: Creating new article from JSON