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💻 '''Open-source model''' in the insurance and [[Definition:Insurtech | insurtech]] sector refers to software, analytical tools, or [[Definition:Machine learning | machine learning]] models whose source code, architecture, and — in the case of data models — training methodologies are publicly available for inspection, modification, and redistribution under open licensing terms. The concept has gained significant traction in insurance as carriers, [[Definition:Managing general agent (MGA) | MGAs]], and technology vendors increasingly rely on [[Definition:Artificial intelligence (AI) | artificial intelligence]], [[Definition:Catastrophe model | catastrophe models]], and [[Definition:Actuarial model | actuarial models]] to price risk, detect [[Definition:Insurance fraud | fraud]], and manage [[Definition:Claims management | claims]]. By contrast with proprietary "black box" solutions — such as the commercial catastrophe models historically provided by firms like [[Definition:RMS | RMS]], [[Definition:AIR Worldwide | AIR Worldwide]], and [[Definition:CoreLogic | CoreLogic]] — open-source alternatives give users the ability to examine, validate, and customize the underlying logic.

🔧 Adoption works through community-driven development and institutional sponsorship. The [[Definition:Oasis Loss Modelling Framework | Oasis Loss Modelling Framework]], for instance, provides an open-source platform for [[Definition:Catastrophe risk | catastrophe risk]] modeling that enables insurers, reinsurers, and regulators to build and run hazard, vulnerability, and financial modules without being locked into a single vendor's assumptions. In the broader technology stack, open-source programming languages and libraries — Python, R, TensorFlow, and scikit-learn among them — underpin much of the [[Definition:Predictive analytics | predictive analytics]] and [[Definition:Risk modeling | risk modeling]] work performed by insurance data science teams worldwide. Open-source large language models have also entered the industry, powering [[Definition:Automated underwriting | automated underwriting]] assistants, document extraction tools, and customer service chatbots. Insurers adopting these models can fine-tune them on proprietary data while retaining transparency over how the model arrives at its outputs — a critical advantage when regulators require explainability in pricing and claims decisions.

🌍 Transparency and auditability are central to why open-source models matter to the insurance industry. Regulators across major markets — including the [[Definition:European Insurance and Occupational Pensions Authority (EIOPA) | EIOPA]], the [[Definition:National Association of Insurance Commissioners (NAIC) | NAIC]], and the [[Definition:Monetary Authority of Singapore (MAS) | Monetary Authority of Singapore]] — have expressed increasing interest in model governance, algorithmic fairness, and the ability of supervisors to interrogate the models that drive [[Definition:Underwriting | underwriting]] and pricing decisions. Open-source models facilitate this oversight because their logic is accessible for independent review, reducing the risk of opaque biases or hidden assumptions that proprietary models may harbor. For smaller insurers and emerging-market players, open-source tools also lower barriers to entry by eliminating steep licensing fees, enabling innovation and competition in an industry where technology costs have historically favored incumbents.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Catastrophe model]]
* [[Definition:Oasis Loss Modelling Framework]]
* [[Definition:Artificial intelligence (AI)]]
* [[Definition:Predictive analytics]]
* [[Definition:Model governance]]
* [[Definition:Insurtech]]
{{Div col end}}

Definition:Open-source model - Revision history

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