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🔎 '''Algorithmic audit''' is a structured evaluation of an automated decision-making system — typically a [[Definition:Machine learning | machine-learning]] or [[Definition:Artificial intelligence (AI) | AI]] model — to assess whether it performs as intended, complies with applicable [[Definition:Insurance regulation | insurance regulations]], and avoids [[Definition:Unfair discrimination | unfairly discriminatory]] outcomes against protected classes. Within the insurance sector, these audits scrutinize models used for [[Definition:Underwriting | underwriting]] eligibility, [[Definition:Algorithmic pricing | algorithmic pricing]], [[Definition:Claims handling | claims triage]], and [[Definition:Fraud detection | fraud scoring]], where flawed or biased outputs can expose carriers to regulatory sanctions, litigation, and reputational harm.

📊 A comprehensive audit typically follows a phased approach. First, auditors document the model's purpose, training data lineage, and feature engineering choices. Next, they run statistical tests — such as [[Definition:Disparate impact | disparate-impact]] ratios, equalized-odds checks, and sensitivity analyses — to determine whether the model treats policyholders or applicants differently based on race, gender, zip code, or other proxies for protected characteristics. The audit also examines operational integrity: Is the model receiving the data it was designed for? Have input distributions shifted since deployment? Finally, auditors produce a findings report with remediation recommendations, which the carrier's [[Definition:Model risk management | model-risk governance]] body reviews before certifying the model for continued use. Colorado's landmark 2023 AI governance regulation, for example, requires [[Definition:Insurance carrier | insurers]] to perform such testing on life-insurance algorithms and report results to the [[Definition:Insurance regulator | division of insurance]].

🛡️ Regular algorithmic audits are rapidly becoming table stakes rather than a voluntary best practice. [[Definition:National Association of Insurance Commissioners (NAIC) | NAIC]] working groups have published model bulletins encouraging states to require that carriers demonstrate ongoing oversight of their [[Definition:Predictive model | predictive models]]. For [[Definition:Insurtech | insurtech]] startups seeking to partner with established carriers, presenting a clean audit trail can accelerate [[Definition:Delegated underwriting authority (DUA) | delegated-authority]] approvals and [[Definition:Reinsurance | reinsurance]] placements. Internally, audits surface data-quality issues and model decay before they materialize as financial or compliance problems — making them as much a tool for operational excellence as for regulatory defense.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Algorithmic accountability]]
* [[Definition:Algorithmic transparency]]
* [[Definition:Model risk management]]
* [[Definition:Unfair discrimination]]
* [[Definition:Predictive model]]
* [[Definition:Regulatory compliance]]
{{Div col end}}

Definition:Algorithmic audit - Revision history

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