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📊 '''Data quality''' refers to the accuracy, completeness, consistency, timeliness, and reliability of data used across insurance operations — from [[Definition:Underwriting | underwriting]] and [[Definition:Actuarial science | actuarial analysis]] to [[Definition:Claims management | claims processing]] and [[Definition:Regulatory compliance | regulatory reporting]]. In an industry where pricing decisions, reserve estimates, and risk selection all hinge on the integrity of underlying information, poor data quality can cascade into mispriced [[Definition:Premium | premiums]], inadequate [[Definition:Loss reserves | loss reserves]], and flawed strategic decisions that threaten an [[Definition:Insurance carrier | insurer's]] financial stability.

🔧 Maintaining high data quality requires deliberate governance at every point where information enters or moves through an insurance organization's systems. Submission data from [[Definition:Insurance broker | brokers]] and [[Definition:Managing general agent (MGA) | MGAs]] must be validated against defined standards before it feeds into pricing models; [[Definition:Bordereaux | bordereaux]] reported under [[Definition:Delegated underwriting authority (DUA) | delegated authority]] arrangements must be reconciled to ensure that bound risks match the terms and limits authorized. [[Definition:Policy administration system | Policy administration systems]], claims platforms, and [[Definition:Data warehouse | data warehouses]] each introduce opportunities for duplication, coding errors, and stale records. Many carriers have established dedicated data stewardship functions that set rules for field-level validation, enforce standardized coding schemes such as [[Definition:Statistical code | statistical codes]] and occupation classifications, and continuously monitor key quality metrics. [[Definition:Insurtech | Insurtech]] firms have also entered this space, offering automated data cleansing, enrichment, and matching tools that integrate directly into carriers' workflows.

💡 The stakes around data quality have intensified as the industry leans more heavily on [[Definition:Predictive analytics | predictive analytics]], [[Definition:Artificial intelligence (AI) | artificial intelligence]], and [[Definition:Machine learning | machine learning]] to drive competitive advantage. Sophisticated models are only as reliable as the data that trains them — a reality that regulators increasingly scrutinize, particularly when algorithms influence [[Definition:Rate making | rate making]] or claims outcomes in ways that could produce unfair discrimination. Beyond regulatory concern, [[Definition:Reinsurance | reinsurers]] and capital partners demand high-quality data as a condition of capacity deployment, meaning that carriers with clean, well-governed data assets enjoy better terms and broader market access. In this sense, data quality functions less as a back-office concern and more as a core competitive capability.

'''Related concepts'''
{{Div col|colwidth=20em}}
* [[Definition:Data science]]
* [[Definition:Predictive analytics]]
* [[Definition:Bordereaux]]
* [[Definition:Actuarial science]]
* [[Definition:Data governance]]
* [[Definition:Policy administration system]]
{{Div col end}}

Definition:Data quality - Revision history

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