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📋 '''Outlier''' in the insurance industry refers to a data point, claim, or observation that falls far outside the expected range established by the rest of a dataset. Whether it surfaces in an [[Definition:Actuarial analysis | actuarial]] loss triangle, a [[Definition:Telematics | telematics]] driving-behavior feed, or a [[Definition:Fraud detection | fraud-detection]] algorithm, an outlier demands scrutiny because it can disproportionately skew statistical conclusions if left unaddressed. Identifying and properly handling outliers is foundational to accurate [[Definition:Insurance pricing | pricing]], [[Definition:Reserving | reserving]], and [[Definition:Risk assessment | risk assessment]].

⚙️ Actuaries and data scientists encounter outliers routinely when analyzing [[Definition:Loss experience | loss experience]]. A single [[Definition:Catastrophe loss | catastrophic claim]] in a small commercial book, for example, can inflate the average [[Definition:Severity (insurance) | severity]] far beyond what is representative of the portfolio's typical risk. Standard techniques for managing outliers include capping or censoring extreme values, applying [[Definition:Large loss loading | large-loss loading]] separately, or using robust statistical methods that down-weight extreme observations. In [[Definition:Predictive modeling | predictive modeling]] for [[Definition:Underwriting | underwriting]] or [[Definition:Claims management | claims triage]], outlier detection serves a dual purpose: it improves model accuracy and can flag potentially [[Definition:Insurance fraud | fraudulent]] or misclassified records for manual review.

🔍 Ignoring outliers — or removing them without justification — introduces its own risks. An unusual cluster of high-[[Definition:Severity (insurance) | severity]] claims in a geographic region might signal an emerging [[Definition:Loss trend | loss trend]], such as a new litigation pattern in [[Definition:Liability insurance | liability]] or an unreported [[Definition:Construction defect | construction defect]] affecting a [[Definition:Builders risk insurance | builders risk]] portfolio. The discipline of outlier analysis is therefore as much about investigation as it is about statistical technique. [[Definition:Insurtech | Insurtech]] platforms leveraging [[Definition:Machine learning | machine learning]] have advanced this practice considerably, enabling real-time anomaly detection across millions of records and surfacing patterns that traditional batch analyses would miss.

'''Related concepts'''
{{Div col|colwidth=20em}}
* [[Definition:Actuarial analysis]]
* [[Definition:Predictive modeling]]
* [[Definition:Severity (insurance)]]
* [[Definition:Fraud detection]]
* [[Definition:Loss trend]]
* [[Definition:Catastrophe loss]]
{{Div col end}}

Definition:Outlier - Revision history

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