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2026-03-19T13:40:00Z

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🤖 '''Training data''' refers to the curated datasets used to build, calibrate, and validate [[Definition:Machine learning | machine learning]] and [[Definition:Artificial intelligence (AI) | artificial intelligence]] models within the insurance industry. In an [[Definition:Insurtech | insurtech]] context, training data might consist of historical [[Definition:Claims | claims]] records, [[Definition:Policy | policy]] attributes, [[Definition:Telematics | telematics]] feeds, medical histories, satellite imagery, or customer interaction logs — any structured or unstructured information from which an algorithm learns patterns to make predictions about [[Definition:Risk | risk]], [[Definition:Pricing | pricing]], [[Definition:Fraud detection | fraud]], or [[Definition:Customer segmentation | customer behavior]]. The quality, representativeness, and volume of this data fundamentally determine whether the resulting model produces reliable outputs or introduces systemic error into insurance operations.

⚙️ When an insurer develops a [[Definition:Predictive model | predictive model]] — for instance, to estimate [[Definition:Loss frequency | claim frequency]] in motor insurance or to flag suspicious [[Definition:Claims | claims]] for investigation — the process begins by assembling a training dataset that reflects the population the model will eventually score. Data engineers clean, label, and sometimes augment these records before feeding them into algorithms such as [[Definition:Gradient boosting | gradient boosting machines]], [[Definition:Neural network | neural networks]], or [[Definition:Random forest | random forests]]. A separate holdout or validation set is used to test the model's accuracy on data it has not seen. In insurance, a critical challenge is dealing with imbalanced datasets: catastrophic events and fraudulent claims are relatively rare, so the training data may contain very few positive examples, requiring techniques like oversampling or synthetic data generation to avoid a model that simply predicts "no event" for every observation.

📋 Regulatory scrutiny of training data is intensifying across major markets. The European Union's [[Definition:AI Act | AI Act]] imposes obligations around data governance and bias testing for high-risk AI applications, many of which fall squarely within insurance [[Definition:Underwriting | underwriting]] and [[Definition:Claims management | claims management]]. In the United States, state [[Definition:Insurance regulator | regulators]] coordinated through the [[Definition:National Association of Insurance Commissioners (NAIC) | NAIC]] have issued guidance on algorithmic fairness that traces directly back to the composition and provenance of training data. Insurers operating in multiple jurisdictions must therefore document not only the statistical properties of their datasets but also how they sourced, consented, and tested them for [[Definition:Proxy discrimination | proxy discrimination]] — ensuring that variables correlated with protected characteristics do not produce unfairly discriminatory outcomes in pricing or claims decisions.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Machine learning]]
* [[Definition:Artificial intelligence (AI)]]
* [[Definition:Predictive model]]
* [[Definition:Algorithmic bias]]
* [[Definition:Data governance]]
* [[Definition:Fraud detection]]
{{Div col end}}

Definition:Training data - Revision history

PlumBot: Bot: Creating new article from JSON