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🖥️ '''Actuarial system''' refers to the specialized software platforms and computational environments that [[Definition:Actuary | actuaries]] within insurance organizations use to model risk, calculate [[Definition:Reserves | reserves]], price [[Definition:Insurance product | products]], and project future financial outcomes. These systems sit at the heart of an insurer's technical operations, processing vast quantities of [[Definition:Policy | policy]], [[Definition:Claim | claims]], and exposure data to produce the quantitative outputs on which [[Definition:Underwriting | underwriting]] decisions, [[Definition:Regulatory capital | capital requirements]], and strategic planning depend. Whether an organization writes [[Definition:Life insurance | life]], [[Definition:Health insurance | health]], [[Definition:Property and casualty insurance | property and casualty]], or [[Definition:Reinsurance | reinsurance]] business, the actuarial system is typically the authoritative source for reserve estimates reported to regulators and in financial statements.

⚙️ Modern actuarial systems range from established vendor platforms — such as those provided by Moody's (formerly RMS), Willis Towers Watson, and FIS — to internally built modeling environments and increasingly cloud-native solutions offered by [[Definition:Insurtech | insurtech]] vendors. A life insurer's system might run stochastic [[Definition:Cash flow projection | cash-flow projections]] across thousands of economic scenarios to comply with [[Definition:IFRS 17 | IFRS 17]] or [[Definition:Solvency II | Solvency II]] requirements, while a non-life carrier's platform may focus on [[Definition:Loss reserving | loss reserving]] triangles, [[Definition:Catastrophe model | catastrophe modeling]] integration, and [[Definition:Rate filing | rate-making]] analytics. The shift toward principles-based reserving standards globally — IFRS 17 in much of the world, [[Definition:Principles-based reserving (PBR) | PBR]] under US statutory rules, and comparable reforms in Japan and China — has driven major system upgrades, as legacy platforms designed for deterministic, formulaic calculations often lack the granularity and scenario-generation capability that newer standards demand.

🔗 Getting the actuarial system right has consequences well beyond the actuarial department. Reserve outputs feed directly into the [[Definition:General ledger | general ledger]] and [[Definition:Financial reporting | financial reporting]] processes; pricing models inform [[Definition:Underwriting guideline | underwriting guidelines]] and [[Definition:Rating algorithm | rating algorithms]]; and capital-modeling results shape conversations with [[Definition:Rating agency | rating agencies]] and [[Definition:Insurance regulator | regulators]]. Poor system performance — whether due to outdated infrastructure, data-quality gaps, or insufficient model governance — can delay filings, introduce material estimation errors, and erode stakeholder confidence. As the industry accelerates its adoption of [[Definition:Artificial intelligence (AI) | artificial intelligence]] and [[Definition:Machine learning | machine learning]], actuarial systems are evolving into broader analytical ecosystems that blend traditional actuarial science with predictive analytics, real-time data feeds, and automated reporting pipelines.

'''Related concepts:'''
{{Div col|colwidth=20em}}
* [[Definition:Actuary]]
* [[Definition:Loss reserving]]
* [[Definition:Catastrophe model]]
* [[Definition:IFRS 17]]
* [[Definition:Principles-based reserving (PBR)]]
* [[Definition:Insurtech]]
{{Div col end}}

Definition:Actuarial system - Revision history

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