How to effectively learn AI Prompting, with the 'AI for Insurance Actuaries (Prompt Course)'?

Start applying AI to actuarial workstreams with confidence

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 will 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.
• Ethical and privacy guardrails for using AI on actuarial projects, including data minimization, anonymization practices, and avoiding sensitive attributes where required.

How the prompts come together as a cohesive course

The course is structured to mirror the actuarial workflow. You begin with foundational practices-prompt patterns for planning, data intake, and quality checks-then move through analytic build, validation, reporting, and governance. Each module adds a layer to your prompt playbook, showing how the same core structures adapt to different insurance contexts. By the end, you have a unified approach that can be applied to life, health, annuity, and property-casualty work.

• Data and experience studies: establish consistent prompts for cleaning, profiling, segmentation, and credibility-weighting discussions.
• Model development: use role, objective, constraints, and acceptance criteria to shape supervised and unsupervised approaches, feature considerations, and calibration routines.
• Pricing and reserving: request evidence-based assumptions, sensitivity frames, and transparency on the trade-offs between simplicity, interpretability, and fit.
• Risk and capital: structure stress and scenario narratives; ask for coherent parameter linkages and documentation of aggregation logic and dependencies.
• Asset-liability management: align model drivers across asset and liability sides; ensure reconciliation steps and audit-ready explanations are part of the outputs.
• Regulatory and solvency assessments: format responses that cover governance, data lineage, validation, and reporting artifacts that withstand scrutiny.
• Strategic outlook modules (climate and technology impact): encourage structured exploratory analysis with clear assumptions, uncertainty ranges, and decision-useful reporting.

Effective use of AI in actuarial work

Actuarial analysis relies on clarity of assumptions, reproducibility, and strong control points. The course teaches a prompt structure that supports these needs:

• Context: provide scope, data definitions, and the specific actuarial objective.
• Constraints: enumerate standards, regulatory references, and practical limits (e.g., data quality, time, materiality thresholds).
• Method options: ask for multiple approaches with pros and cons, including simple baselines and more sophisticated alternatives.
• Validation plan: specify the diagnostics and reasonableness checks you expect to see.
• Deliverables: define the format, exhibits, and documentation requirements for stakeholders.
• Quality gates: require checklists and callouts of limitations, so reviewers know where to focus.

These elements keep the AI focused, reduce ambiguity, and create outputs that are easier to review and reuse.

How each module adds value

• Mortality and morbidity analysis: establish standardized workflows for incidence/severity analysis, improvement factors, and peer-review documentation.
• Financial forecasting: structure multi-period projections with transparent drivers, sensitivities, and narrative reporting that ties numbers to business rationale.
• Risk modeling: guide end-to-end model development from problem framing to validation and monitoring, with explicit considerations around interpretability and governance.
• Pricing strategy: balance technical results with market insights, explain rate indications, and produce decision-ready summaries for product committees.
• Reserving methodologies: set prompts that encourage cross-method reconciliation, reasonableness checks, and clear explanation of selections.
• Catastrophe risk: support scenario framing, exposure data checks, and aggregation narratives that clarify uncertainty and model limitations.
• Regulatory assessment: produce structured responses aligned with compliance expectations, including documentation templates and evidence logs.
• Predictive analytics: encourage transparent feature logic, fairness checks where relevant, and reproducible experiment documentation.
• Asset-liability management: request coherent linkage of asset and liability assumptions, explain mismatches, and prepare board-level briefs.
• Policyholder behavior: frame hypothesis-driven analysis, segmentation, and monitoring plans for lapse, utilization, or claim behavior.
• Annuity product development: connect pricing, hedging, and capital considerations with clear communication of trade-offs.
• Solvency assessment: align risk measures, aggregation, and reporting artifacts to support internal and external stakeholders.
• Experience studies: operationalize a reusable study workflow that emphasizes data governance, documentation, and trend interpretation.
• Climate impact: create structured long-horizon scenario narratives with assumptions, data sources, and decision-useful outputs.
• Technological impact: assess operational and risk implications of new tools with transparent assumptions and measurable KPIs.

Who this course is for

• Life, health, annuity, and property-casualty actuaries involved in pricing, reserving, capital, or product.
• Risk managers, model validation teams, and actuarial auditors seeking consistent AI-aided documentation.
• Leads establishing AI usage standards, governance, and repeatable analytics playbooks.

Prerequisites and expected background

Participants should be comfortable with core actuarial concepts and have familiarity with spreadsheets and basic analytics. Experience with R, Python, or SQL is helpful but not required. The course focuses on making AI outputs dependable and reviewable, rather than replacing actuarial judgment.

Quality, governance, and ethics

• Data privacy: learn how to minimize sensitive data, anonymize where feasible, and document compliance considerations.
• Model risk controls: set acceptance criteria, verification steps, and model-change logs that feed into governance frameworks.
• Bias and fairness: include checks for unwanted proxy effects and document limitations openly.
• Audit trails: insist on citations, formula references, and versioned outputs that are easy to trace.

How this course saves time without sacrificing rigor

• Reusable prompt templates that standardize analysis and reduce rework.
• Built-in checklists that surface limitations and assumptions early in the process.
• Clear instructions for generating exhibits, memos, and board materials from the same analytical core.
• Guidance for integrating with code and spreadsheets, accelerating model iteration and documentation.

What you will take away

• A structured prompt playbook covering the actuarial lifecycle-from experience studies and modeling to pricing, reserving, capital, compliance, and strategic assessments.
• Governance-ready documentation patterns that help with peer review, audits, and regulator questions.
• A practical approach for using AI assistants that emphasizes transparency, validation, and professional oversight.

Course format and learning flow

• Concept primers: short, targeted explanations of how AI fits into specific actuarial workstreams.
• Prompt frameworks: repeatable structures you can apply across different lines, geographies, and regulatory contexts.
• Outcome-focused exercises: tasks that produce checklists, memos, dashboards, and summaries aligned to real actuarial outputs.
• Review and refinement: techniques for critiquing AI outputs, tightening the prompts, and improving reproducibility.

How the modules interconnect

The modules intentionally reference one another. For example, experience study prompts feed into modeling prompts; modeling diagnostics feed into pricing or reserving choices; ALM prompts align with solvency and board reporting; regulatory prompts wrap around each step to keep documentation consistent. This connected approach means you do not end up with isolated scripts but rather a cohesive method that supports end-to-end actuarial delivery.

Why this approach works

• Consistency: standardized structures reduce ambiguity and help teams work in a common format.
• Transparency: required assumptions, methods, and checks are explicit and easy to review.
• Adaptability: the same pattern scales from quick ad-hoc tasks to large, cross-functional analyses.
• Governance: the outputs map neatly into model risk, audit, and regulatory expectations.

Getting started

If you want AI to help with actuarial work without compromising standards, this course gives you a clear, structured path. Begin with the foundational modules on planning and data controls, then move through analysis, validation, and reporting. By the end, you will have a dependable prompt playbook and a set of practices that support quality, speed, and accountability across your actuarial portfolio.