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🤖 '''Machine learning''' is a branch of [[Definition:Artificial intelligence | artificial intelligence]] in which algorithms improve their performance on a task by learning from data rather than following explicitly programmed rules. In [[Definition:Insurance | insurance]], machine learning is being applied across the value chain — from [[Definition:Underwriting | underwriting]] and [[Definition:Pricing model | pricing]] to [[Definition:Fraud detection | fraud detection]], [[Definition:Claims handling | claims triage]], and [[Definition:Customer experience | customer engagement]] — wherever large data sets and pattern recognition can enhance speed, accuracy, or efficiency.

🔧 The techniques range in complexity. Supervised models such as gradient-boosted trees and neural networks are trained on labeled [[Definition:Loss data | historical data]] to predict outcomes like [[Definition:Insurance claim | claim]] frequency, severity, or [[Definition:Policyholder | policyholder]] retention. Unsupervised methods detect hidden structure — clustering similar risks or flagging anomalous [[Definition:Insurance claim | claims]] that may warrant investigation. Natural language processing extracts information from unstructured sources like [[Definition:Submission | submission]] documents, medical records, and adjuster notes, accelerating workflows that were previously manual. In each case, the model's value depends on the quality and representativeness of the training data, the rigor of [[Definition:Model validation | model validation]], and the thoughtfulness of its deployment within existing decision processes.

⚖️ Adoption brings significant promise but also real governance challenges. A model that improves [[Definition:Loss ratio (L/R) | loss ratio]] performance by selecting risk more precisely could simultaneously introduce [[Definition:Algorithmic bias | algorithmic bias]] or violate [[Definition:Unfair discrimination | fair discrimination]] standards if its features serve as proxies for [[Definition:Protected class | protected characteristics]]. [[Definition:Insurance regulator | Regulators]] — including the NAIC and European supervisory authorities — are developing frameworks that require [[Definition:Insurance carrier | insurers]] to explain model outputs, monitor for [[Definition:Disparate impact | disparate impact]], and maintain human oversight of consequential decisions. For [[Definition:Insurtech | insurtech]] firms and established carriers alike, building robust [[Definition:Model risk management | model governance]] around machine learning is becoming as important as building the models themselves.

'''Related concepts'''
{{Div col|colwidth=20em}}
* [[Definition:Artificial intelligence]]
* [[Definition:Predictive analytics]]
* [[Definition:Algorithmic bias]]
* [[Definition:Model validation]]
* [[Definition:Model risk management]]
* [[Definition:Natural language processing (NLP)]]
{{Div col end}}

Definition:Machine learning - Revision history

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