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🤖 '''Automated decision''' refers to a determination made by an algorithm, rules engine, or [[Definition:Artificial intelligence (AI) | artificial intelligence]] model without meaningful human intervention at the point of decision, applied across the insurance value chain in areas such as [[Definition:Underwriting | underwriting]], [[Definition:Claims management | claims handling]], [[Definition:Fraud detection | fraud detection]], and [[Definition:Pricing | pricing]]. In insurance, automated decisions range from straightforward rules-based actions — like instantly approving a travel insurance application that meets predefined criteria — to complex [[Definition:Machine learning | machine learning]]-driven outputs that assess risk profiles or determine [[Definition:Claims settlement | claims settlements]]. As [[Definition:Insurtech | insurtech]] capabilities mature, the volume and significance of decisions delegated to automated systems continue to grow across global markets.

⚙️ These decisions operate through a spectrum of technological approaches. At the simpler end, a [[Definition:Business rules engine | business rules engine]] might automatically decline a [[Definition:Motor insurance | motor insurance]] quote if the applicant falls outside a defined age or claims-history band. At the more sophisticated end, [[Definition:Predictive model | predictive models]] trained on historical data score submissions for [[Definition:Risk | risk]] quality, flag potentially [[Definition:Insurance fraud | fraudulent]] claims, or dynamically adjust [[Definition:Premium | premiums]] based on real-time behavioral data from [[Definition:Telematics | telematics]] devices or [[Definition:Internet of Things (IoT) | IoT]] sensors. The governance challenge is ensuring these systems remain accurate, fair, and explainable. Regulators across jurisdictions increasingly demand transparency: the European Union's [[Definition:General Data Protection Regulation (GDPR) | GDPR]] grants individuals the right not to be subject to solely automated decisions with legal or significant effects, and insurance-specific supervisors — including [[Definition:European Insurance and Occupational Pensions Authority (EIOPA) | EIOPA]] and the [[Definition:National Association of Insurance Commissioners (NAIC) | NAIC]] in the US — have issued guidance on [[Definition:Algorithmic accountability | algorithmic accountability]] and the use of [[Definition:Big data | big data]] in insurance.

⚠️ The stakes around automated decisions in insurance are high because these determinations directly affect individuals' access to coverage and financial recovery after a loss. A biased model could systematically overcharge certain demographic groups or unfairly deny legitimate claims, exposing insurers to regulatory action, reputational damage, and litigation. Conversely, well-governed automation accelerates service delivery, reduces costs, and improves consistency — a [[Definition:Straight-through processing (STP) | straight-through processing]] pipeline can settle a simple [[Definition:Home insurance | home insurance]] claim in hours rather than weeks. The industry is converging on the principle that automated decisions require robust [[Definition:Model risk management | model validation]], ongoing monitoring for drift and bias, clear escalation paths to human reviewers, and transparent communication to policyholders about how decisions are made. Getting this balance right is central to maintaining public trust as insurance becomes increasingly digitized.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Artificial intelligence (AI)]]
* [[Definition:Machine learning]]
* [[Definition:Straight-through processing (STP)]]
* [[Definition:Algorithmic accountability]]
* [[Definition:Predictive model]]
* [[Definition:Model risk management]]
{{Div col end}}

Definition:Automated decision - Revision history

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