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📊 '''Historical loss data''' is the body of quantified financial information reflecting the actual [[Definition:Loss | losses]] an insurer, [[Definition:Reinsurer | reinsurer]], or insured entity has experienced over a defined period, typically expressed in dollar amounts and organized by policy year, accident year, or calendar year. While closely related to [[Definition:Historical claims data | historical claims data]], loss data focuses specifically on the monetary dimension — paid losses, [[Definition:Incurred loss | incurred losses]], [[Definition:Loss adjustment expense (LAE) | loss adjustment expenses]], and ultimate loss estimates — rather than the full operational detail of each claim file. Insurers depend on this information to calibrate [[Definition:Pricing model | pricing models]], evaluate [[Definition:Reinsurance treaty | reinsurance treaty]] performance, and benchmark their results against industry aggregates.

🔧 In practice, historical loss data fuels the [[Definition:Experience rating | experience rating]] process, where an insured's own track record directly influences the [[Definition:Premium | premium]] it pays. For [[Definition:Workers' compensation insurance | workers' compensation]] or [[Definition:General liability insurance | general liability]] programs, [[Definition:Underwriter | underwriters]] request multiple years of loss runs — typically five to ten — and analyze trends in [[Definition:Loss frequency | frequency]] and [[Definition:Loss severity | severity]] before quoting. [[Definition:Actuary | Actuaries]] apply [[Definition:Loss development factor | loss development factors]] to immature years to project ultimate costs, ensuring that recent claims still working through the system are not underestimated. At the portfolio level, aggregated historical loss data underpins [[Definition:Catastrophe model | catastrophe models]], [[Definition:Capital adequacy | capital adequacy]] assessments, and strategic decisions about which [[Definition:Line of business | lines of business]] to grow, shrink, or exit.

💡 Gaps or distortions in historical loss data can have outsized consequences. If an insurer acquires a [[Definition:Book of business | book of business]] without adequate loss history, it risks inheriting unknown liabilities or mispricing renewals. Similarly, when [[Definition:Insurtech | insurtechs]] enter underserved markets where loss data is thin, they often supplement limited internal experience with [[Definition:Third-party data | third-party data]] or [[Definition:Synthetic data | synthetic data]] to build credible models. Regulatory bodies also scrutinize how carriers use loss data in rate filings, insisting that assumptions are transparent and that adjustments for [[Definition:Trend factor | trend]], [[Definition:Inflation | inflation]], and changes in legal environment are documented. In short, the integrity of historical loss data is inseparable from the integrity of the pricing promise an insurer makes to its market.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Historical claims data]]
* [[Definition:Experience rating]]
* [[Definition:Loss development factor]]
* [[Definition:Loss run]]
* [[Definition:Actuarial analysis]]
* [[Definition:Incurred loss]]
{{Div col end}}

Definition:Historical loss data - Revision history

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