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📊 '''Loss data''' is the structured body of historical information that records the details of [[Definition:Insurance claim | claims]] and [[Definition:Loss | losses]] experienced by an [[Definition:Insurance carrier | insurer]], [[Definition:Reinsurer | reinsurer]], or insured entity. A typical loss data set captures attributes such as date of loss, date reported, [[Definition:Line of business | line of business]], [[Definition:Coverage part | coverage part]], cause of loss, paid amounts, [[Definition:Claim reserve | case reserves]], claimant details, and claim status — providing the empirical foundation for virtually every analytical function in the insurance value chain.

🔬 [[Definition:Actuary | Actuaries]] rely on loss data to build [[Definition:Loss development triangle | loss development triangles]], calibrate [[Definition:Frequency-severity model | frequency-severity models]], and estimate [[Definition:Ultimate loss | ultimate loss]] costs. [[Definition:Underwriter | Underwriters]] use it to evaluate the historical performance of a [[Definition:Risk | risk]] or [[Definition:Book of business | book of business]] before quoting renewal terms. [[Definition:Catastrophe modeling | Catastrophe modelers]] match it against [[Definition:Hazard | hazard]] simulations to validate assumptions, and [[Definition:Data scientist | data scientists]] feed it into [[Definition:Machine learning | machine learning]] algorithms to improve [[Definition:Predictive analytics | predictive accuracy]]. The quality of all these outputs hinges on the completeness, consistency, and granularity of the underlying data — a reality that elevates [[Definition:Data governance | data governance]] from a back-office function to a strategic priority.

🔑 Gaps and inconsistencies in loss data can cascade through an organization. Under-reserved claims, miscoded causes of loss, or missing policy-level fields introduce noise that degrades [[Definition:Pricing model | pricing models]], distorts [[Definition:Loss ratio (L/R) | loss ratio]] analysis, and undermines [[Definition:Due diligence | due diligence]] conclusions during portfolio transfers or [[Definition:Mergers and acquisitions (M&A) | M&A]] transactions. As the industry pushes toward [[Definition:Straight-through processing (STP) | straight-through processing]] and real-time analytics, the pressure to capture clean, structured loss data at the point of origin — rather than correcting it after the fact — has become a defining theme in insurance operations and technology investment.

'''Related concepts'''
{{Div col|colwidth=20em}}
* [[Definition:Loss development triangle]]
* [[Definition:Loss experience]]
* [[Definition:Data governance]]
* [[Definition:Actuarial analysis]]
* [[Definition:Claim reserve]]
* [[Definition:Bordereau]]
{{Div col end}}

Definition:Loss data - Revision history

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