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🧠 '''Unsupervised learning''' is a branch of [[Definition:Machine learning | machine learning]] in which algorithms identify patterns, groupings, or structures within data without being provided labeled outcomes — a technique increasingly deployed across insurance for tasks where predefined categories do not exist or would be impractical to create manually. Unlike [[Definition:Supervised learning | supervised learning]], which requires [[Definition:Training data | training data]] with known answers (e.g., "this claim was fraudulent" or "this policy lapsed"), unsupervised methods explore the data's inherent structure. In insurance, this makes them particularly valuable for [[Definition:Customer segmentation | customer segmentation]], [[Definition:Anomaly detection | anomaly detection]] in [[Definition:Claims | claims]] portfolios, and discovering emerging [[Definition:Risk | risk]] clusters that traditional [[Definition:Actuarial science | actuarial]] classifications may not yet capture.

🔍 Common unsupervised techniques applied in insurance include clustering algorithms (such as k-means or DBSCAN) that group [[Definition:Policyholder | policyholders]] with similar behavioral or risk profiles, and dimensionality reduction methods (like principal component analysis) that distill large feature sets into manageable representations. A [[Definition:Property and casualty insurance | property and casualty]] insurer might use clustering to segment its commercial book into natural peer groups for [[Definition:Pricing | pricing]] refinement, while a [[Definition:Health insurance | health insurer]] could apply anomaly detection to flag unusual billing patterns that warrant [[Definition:Fraud detection | fraud investigation]]. Because these models do not require pre-labeled fraud cases or loss outcomes, they can surface previously unknown patterns — a significant advantage when dealing with novel exposures like [[Definition:Cyber insurance | cyber risk]], where historical labeled data remains scarce.

💡 Adoption of unsupervised learning does, however, introduce interpretability challenges that resonate throughout insurance regulation. [[Definition:Insurance regulator | Regulators]] in the European Union, guided by the [[Definition:AI Act | AI Act]] and [[Definition:Solvency II | Solvency II]] governance expectations, and in the United States through [[Definition:National Association of Insurance Commissioners (NAIC) | NAIC]] model bulletins, increasingly require insurers to explain how algorithmic decisions affect [[Definition:Underwriting | underwriting]] and [[Definition:Claims management | claims outcomes]]. Unsupervised models, by their nature, produce outputs whose business meaning may not be immediately obvious — a cluster is a mathematical grouping, not an intuitive risk category. Insurers therefore often pair unsupervised techniques with human expert review or secondary [[Definition:Supervised learning | supervised models]] to translate discovered patterns into actionable, explainable decisions that satisfy both business needs and regulatory expectations.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Supervised learning]]
* [[Definition:Machine learning]]
* [[Definition:Anomaly detection]]
* [[Definition:Customer segmentation]]
* [[Definition:Predictive model]]
* [[Definition:Algorithmic bias]]
{{Div col end}}

Definition:Unsupervised learning - Revision history

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