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📋 '''Black box model''' is a predictive or decision-making model whose internal logic is opaque — meaning that while it produces outputs from given inputs, the reasoning pathway connecting the two is not readily interpretable by humans. In the insurance industry, the term most often refers to complex [[Definition:Machine learning (ML) | machine learning]] algorithms — such as deep neural networks, gradient-boosted trees, or ensemble methods — used in [[Definition:Underwriting | underwriting]], [[Definition:Claims management | claims]] triage, [[Definition:Fraud detection | fraud detection]], and [[Definition:Pricing | pricing]], where the model's predictive power comes at the cost of transparency about how individual factors drive a particular decision.

⚙️ These models typically ingest large volumes of data — [[Definition:Loss history | loss history]], policyholder demographics, telematics feeds, geospatial information, or unstructured text from [[Definition:Claims management | claims]] files — and identify patterns that traditional [[Definition:Actuarial science | actuarial]] models, such as [[Definition:Generalized linear model (GLM) | generalized linear models]], may miss. A black box model might, for example, assign a risk score to a commercial property submission by weighing hundreds of interacting variables simultaneously, producing a more granular prediction than a conventional rating algorithm. The challenge is that neither the [[Definition:Underwriter | underwriter]] relying on the score nor the [[Definition:Policyholder | policyholder]] affected by the decision can easily trace why the model arrived at a particular output. Techniques collectively known as [[Definition:Explainable artificial intelligence (XAI) | explainable AI]] — including SHAP values, LIME, and partial dependence plots — have emerged to crack open these black boxes and provide post-hoc interpretability, but they add complexity and do not fully replicate the intuitive transparency of simpler models.

💡 Regulatory scrutiny of black box models in insurance is intensifying across jurisdictions. In the European Union, the [[Definition:Solvency II | Solvency II]] framework and the emerging AI Act impose governance and transparency requirements on automated decision-making. U.S. state regulators and the [[Definition:National Association of Insurance Commissioners (NAIC) | NAIC]] have focused on ensuring that [[Definition:Algorithmic underwriting | algorithmic underwriting]] and pricing models do not produce unfairly discriminatory outcomes — a concern amplified when the model's logic cannot be audited in traditional ways. In markets such as Singapore and Hong Kong, financial regulators have issued principles on responsible AI use that apply to insurers. The core tension is real: black box models often outperform interpretable alternatives on pure predictive accuracy, giving insurers that use them a competitive edge in [[Definition:Risk selection | risk selection]] and [[Definition:Loss ratio (L/R) | loss ratio]] management. Yet the inability to explain a coverage denial or premium increase to a regulator, a court, or a customer creates legal, reputational, and ethical risks that every insurer deploying these tools must carefully manage.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Explainable artificial intelligence (XAI)]]
* [[Definition:Machine learning (ML)]]
* [[Definition:Generalized linear model (GLM)]]
* [[Definition:Algorithmic underwriting]]
* [[Definition:Fraud detection]]
* [[Definition:Actuarial science]]
{{Div col end}}

Definition:Black box model - Revision history

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