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🧠 '''Expert judgment''' in insurance refers to the informed, structured application of professional knowledge and experience to make decisions or produce estimates where data is absent, insufficient, ambiguous, or where models alone cannot provide a reliable answer. Across the insurance industry — from [[Definition:Reserving | reserving]] and [[Definition:Underwriting | underwriting]] to [[Definition:Catastrophe modeling | catastrophe modeling]] and [[Definition:Capital modeling | capital adequacy assessment]] — practitioners regularly encounter situations where historical data is sparse (such as emerging [[Definition:Cyber insurance | cyber risks]]), where model outputs require qualitative overlay, or where regulatory frameworks explicitly demand documented human reasoning. Far from being an informal "gut feel," expert judgment as recognized under regimes like [[Definition:Solvency II | Solvency II]] and [[Definition:IFRS 17 | IFRS 17]] must follow a disciplined, auditable process.

🔧 Deploying expert judgment effectively requires a governance framework that specifies when it should be invoked, who is qualified to exercise it, how the reasoning is documented, and how the resulting estimates are validated over time. Under Solvency II, [[Definition:EIOPA | EIOPA]] guidelines make clear that expert judgment used in calculating [[Definition:Technical provisions | technical provisions]] or [[Definition:Solvency capital requirement (SCR) | SCR]] figures must be supported by a rationale that references relevant data, experience, and methodology — and the [[Definition:Actuarial function | actuarial function]] is typically responsible for ensuring its appropriateness. Common applications include selecting [[Definition:Loss development factor | loss development factors]] for long-tail [[Definition:Liability insurance | liability lines]], calibrating [[Definition:Assumption | assumptions]] for mortality improvement trends in [[Definition:Life insurance | life insurance]], adjusting model outputs for known but unmodeled exposures, and setting [[Definition:Prior year reserve | reserve]] estimates for newly emerged [[Definition:Loss event | loss events]] before credible claims data accumulates. Techniques such as Delphi panels, structured elicitation protocols, and scenario workshops help reduce individual cognitive bias and improve the robustness of the judgments produced.

📌 The role of expert judgment in insurance has come under heightened scrutiny as the industry embraces [[Definition:Artificial intelligence (AI) | artificial intelligence]] and data-driven decision-making. Rather than displacing human judgment, advanced analytics tend to shift its application — from routine estimation tasks, where algorithms now excel, to higher-order questions about model selection, assumption validation, and the interpretation of novel risks. [[Definition:Insurance regulator | Regulators]] worldwide expect firms to maintain clear documentation distinguishing between model-driven outputs and expert overlays, particularly in [[Definition:Internal model | internal model]] approval processes and [[Definition:Own risk and solvency assessment (ORSA) | ORSA]] submissions. Poorly governed expert judgment has historically been a source of reserving errors and supervisory censure, making it a perennial focus of [[Definition:External audit | external auditors]] and regulatory examinations. Whether an actuary in Tokyo is estimating [[Definition:Earthquake insurance | earthquake]] tail risk or an underwriter in London is pricing a novel [[Definition:Product liability insurance | product liability]] exposure, the ability to apply expert judgment transparently and defensibly remains one of the most valued — and scrutinized — competencies in the profession.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Actuarial function]]
* [[Definition:Technical provisions]]
* [[Definition:Internal model]]
* [[Definition:Own risk and solvency assessment (ORSA)]]
* [[Definition:Assumption]]
* [[Definition:Reserving]]
{{Div col end}}

Definition:Expert judgment - Revision history

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