AI for Insurance Actuaries (Prompt Course)
15 lessons · 261 prompts
A day in the life of an Insurance Actuary: what changes with these prompts.
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AI for Insurance Actuaries (Prompt Course) is a practical, end-to-end learning experience that shows actuaries how to use AI assistants responsibly across core functions-from mortality and morbidity studies to pricing, reserving, solvency analysis, regulatory reviews, and strategic assessments. Rather than offering abstract theory, this course focuses on how to structure interactions with AI so that outputs are transparent, auditable, and aligned with actuarial standards. You will build a coherent prompt playbook that fits your team's workflows, improves documentation, and reduces rework-without compromising the judgment and oversight that remain central to actuarial practice.
What you'll learn
- How to set up repeatable AI workflows for actuarial tasks: structuring context, constraints, assumptions, references, and acceptance criteria so analyses are consistent and easy to review.
- Ways to guide AI to produce documentation that supports peer review: summaries of assumptions, alternative methods considered, validation steps, and clear rationale trails.
- Approaches for integrating AI into existing toolchains (Excel, R, Python, SQL, BI platforms) by asking for code scaffolds, dataset checks, QA routines, and explanatory notes.
- Methods to translate model outputs into stakeholder-ready exhibits and memos, including sensitivity views, scenario narratives, and board-level briefing formats.
- Practical techniques to reduce AI errors: fact-checking, cross-verification against known formulas, staged prompts with checkpoints, and controlled comparison of model alternatives.
- How to improve experience studies with structured prompts for data quality checks, segmentation design, credibility considerations, and monitoring plans.
- How to ask for model diagnostics and reasonableness checks that echo actuarial review standards, including out-of-sample tests, stability checks, and backtesting over business cycles.
- How to align AI-supported work with regulatory expectations: documenting governance, model changes, data lineage, key controls, and audit-ready explanations.
How this course works
- 15 lessonsOne task of your job each, from mortality and morbidity analysis to technological advancement impact.
- Ready-to-paste promptsCopy, fill in the parts in {{brackets}}, paste into ChatGPT, Claude or Gemini.
- Tick and completeTick the prompts you tried and mark each lesson complete.
- Get certifiedFinish with the exam and a certificate for LinkedIn.