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🤖 '''Algorithm''' in insurance refers to a defined set of computational rules or instructions that processes data to produce a decision, score, or output — most commonly applied in [[Definition:Underwriting | underwriting]], [[Definition:Pricing | pricing]], [[Definition:Fraud detection | fraud detection]], and [[Definition:Claims management | claims management]]. While algorithms exist across every technology-dependent industry, their role in insurance carries particular weight because the outputs directly determine who gets covered, at what price, and how quickly claims are resolved. From simple decision trees embedded in legacy [[Definition:Rating engine | rating engines]] to sophisticated [[Definition:Machine learning | machine learning]] models, algorithms are the invisible machinery behind modern insurance operations.

🔧 A [[Definition:Pricing | pricing]] algorithm, for example, ingests variables — age, location, claims history, telematics scores, property characteristics — and applies mathematical functions to produce a [[Definition:Premium | premium]] quote. In [[Definition:Claims management | claims triage]], algorithms score incoming notifications by predicted severity and [[Definition:Fraud | fraud]] likelihood, routing straightforward cases to [[Definition:Straight-through processing (STP) | straight-through processing]] and flagging outliers for human review. [[Definition:Insurtech | Insurtech]] firms have pushed algorithmic sophistication further by incorporating [[Definition:Alternative data | alternative data]] sources — satellite imagery, social media signals, IoT sensor feeds — into models that traditional actuarial approaches never contemplated. The speed and consistency that algorithms bring to high-volume decisions is a core driver of operational efficiency, especially in personal lines and [[Definition:Small commercial insurance | small commercial]] segments where margins depend on processing scale.

⚖️ With greater reliance on algorithmic decision-making comes intensifying regulatory scrutiny. Supervisory bodies in the EU, the UK, and several US states have begun examining whether insurance algorithms produce outcomes that are [[Definition:Unfair discrimination | unfairly discriminatory]] — for instance, if a [[Definition:Machine learning | machine learning]] model inadvertently uses proxies for race, gender, or income in ways that violate anti-discrimination law. The concept of [[Definition:Algorithmic transparency | algorithmic transparency]] and the demand for "explainable AI" are moving from academic discussion into concrete regulatory expectations. Insurers that deploy algorithms without robust [[Definition:Model governance | model governance]], bias testing, and audit trails risk not only regulatory sanctions but also reputational damage and erosion of consumer trust. Responsible algorithmic design is therefore becoming as much a compliance imperative as it is a competitive advantage.

'''Related concepts'''
{{Div col|colwidth=20em}}
* [[Definition:Machine learning]]
* [[Definition:Artificial intelligence (AI)]]
* [[Definition:Rating engine]]
* [[Definition:Predictive analytics]]
* [[Definition:Algorithmic transparency]]
* [[Definition:Straight-through processing (STP)]]
{{Div col end}}

Definition:Algorithm - Revision history

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