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🧠 '''Neural network''' is a computational model inspired by the structure of biological neurons, increasingly deployed across the insurance industry to recognize complex patterns in data that traditional statistical methods struggle to capture. In insurance contexts, neural networks power applications ranging from [[Definition:Automated underwriting | automated underwriting]] and [[Definition:Claims management | claims]] fraud detection to [[Definition:Pricing model | pricing optimization]] and [[Definition:Customer segmentation | customer segmentation]]. Unlike simpler [[Definition:Predictive analytics | predictive models]] such as generalized linear models (GLMs), neural networks can learn nonlinear relationships among hundreds or thousands of input variables, making them especially valuable when insurers face heterogeneous risk pools or unstructured data sources like images, text, and telematics streams.

⚙️ A neural network operates through layers of interconnected nodes — an input layer that receives data (such as policyholder attributes, claim histories, or sensor readings), one or more hidden layers that transform and weight these inputs, and an output layer that produces a prediction or classification. During training, the network adjusts the weights connecting its nodes by comparing its outputs against known outcomes, iteratively minimizing error through a process called backpropagation. In practice, an insurer might feed a deep neural network millions of historical [[Definition:Loss experience | loss records]] to build a [[Definition:Risk scoring | risk-scoring]] engine, or train a convolutional neural network on vehicle damage photographs to automate [[Definition:Claims adjudication | claims adjudication]] estimates. The model's accuracy generally improves with larger and richer datasets, which is why [[Definition:Insurtech | insurtech]] firms and large carriers investing in [[Definition:Data analytics | data infrastructure]] have been among the earliest adopters.

🔍 Regulatory scrutiny, however, tempers enthusiasm. Neural networks are often described as "black boxes" because explaining precisely why a model reached a particular decision can be difficult — a concern that matters greatly in insurance, where regulators in jurisdictions governed by [[Definition:Solvency II | Solvency II]], the [[Definition:National Association of Insurance Commissioners (NAIC) | NAIC]]'s model laws, and other frameworks require that [[Definition:Rate filing | rating factors]] be transparent, actuarially justified, and free from unfair [[Definition:Discrimination | discrimination]]. Techniques such as SHAP values and LIME have emerged to improve model explainability, and several regulators — including those in the EU under the AI Act — are developing specific guidance on the use of [[Definition:Artificial intelligence (AI) | artificial intelligence]] in insurance pricing and claims. Balancing the predictive power of neural networks against the obligation to treat customers fairly remains one of the defining challenges as the technology matures across global insurance markets.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Artificial intelligence (AI)]]
* [[Definition:Machine learning (ML)]]
* [[Definition:Predictive analytics]]
* [[Definition:Telematics]]
* [[Definition:Insurtech]]
* [[Definition:Algorithmic underwriting]]
{{Div col end}}

Definition:Neural network - Revision history

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