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🗄️ '''Exposure database''' is a structured repository containing detailed information about the risks an [[Definition:Insurance carrier | insurer]] or [[Definition:Reinsurer | reinsurer]] has on its books — including policy locations, insured values, construction types, occupancy classes, and other attributes needed to assess vulnerability to [[Definition:Loss | loss]]. In [[Definition:Property insurance | property]] and [[Definition:Catastrophe reinsurance | catastrophe reinsurance]], the exposure database is the foundational input for [[Definition:Catastrophe model | catastrophe models]], enabling carriers to estimate potential losses from events such as hurricanes, earthquakes, floods, and wildfires. Maintaining a high-quality exposure database is not merely a best practice — regulators in [[Definition:Solvency II | Solvency II]] jurisdictions, under the [[Definition:Risk-based capital (RBC) | RBC]] framework in the United States, and within regimes like [[Definition:China Risk Oriented Solvency System (C-ROSS) | C-ROSS]] in China all expect firms to demonstrate they understand their aggregate exposures.

⚙️ Building and maintaining an exposure database involves ingesting data from multiple sources: policy administration systems, [[Definition:Bordereaux | bordereaux]] reports from [[Definition:Managing general agent (MGA) | MGAs]] and [[Definition:Coverholder | coverholders]], [[Definition:Broker | broker]] submissions, and third-party geocoding and enrichment services. Each record typically includes the geographic coordinates (or at minimum a postal code) of the insured property, the [[Definition:Total insured value (TIV) | total insured value]], the type of coverage, policy terms such as [[Definition:Deductible | deductibles]] and [[Definition:Sublimit | sublimits]], and physical characteristics of the structure. Data quality challenges are pervasive: incomplete addresses, missing construction details, and inconsistent formatting across different source systems can degrade modeling accuracy. [[Definition:Insurtech | Insurtech]] vendors have emerged to address these gaps, offering automated validation, geocoding, and enrichment tools that cleanse raw exposure data before it enters catastrophe modeling platforms from firms like [[Definition:Moody's RMS | Moody's RMS]], [[Definition:Verisk | Verisk]], and [[Definition:CoreLogic | CoreLogic]].

🔍 The quality of an exposure database directly determines how much confidence [[Definition:Underwriter | underwriters]], [[Definition:Actuary | actuaries]], and senior management can place in their [[Definition:Probable maximum loss (PML) | probable maximum loss]] estimates and [[Definition:Accumulation risk | accumulation]] controls. Poor data leads to model output that understates or overstates risk, with potentially severe consequences: underestimation can leave a company dangerously underreserved heading into a catastrophe season, while overestimation ties up capital unnecessarily and erodes competitiveness. Rating agencies such as [[Definition:AM Best | AM Best]] and [[Definition:S&P Global Ratings | S&P Global Ratings]] routinely scrutinize exposure data governance as part of their [[Definition:Enterprise risk management (ERM) | enterprise risk management]] assessments, and major reinsurers increasingly require cedents to submit exposure data in standardized formats as a condition of treaty placement.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Catastrophe model]]
* [[Definition:Total insured value (TIV)]]
* [[Definition:Accumulation risk]]
* [[Definition:Probable maximum loss (PML)]]
* [[Definition:Bordereaux]]
* [[Definition:Data quality]]
{{Div col end}}

Definition:Exposure database - Revision history

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