Practical applications of gen AI in actuarial work

In an era defined by rapid technological advancement, generative artificial intelligence (gen AI) is poised to revolutionize actuarial work. Actuarial professionals are increasingly exploring gen AI as a transformative addition to their tool kit. Gen AI refers to advanced machine learning models capable of generating coherent, humanlike content, ranging from document drafts to interpretations of complex queries. In an insurance context, its value lies in handling time-consuming, text-heavy and analytical tasks, enabling actuaries to shift their focus toward higher-value, judgment-driven work.

This shift is timely. Today’s actuaries operate under growing pressure—facing intricate model requirements, tighter reporting cycles and evolving regulatory expectations. Gen AI presents an opportunity to enhance both efficiency and insight: It can summarize datasets, produce consistent documentation and translate technical findings into clear, business-relevant language. Importantly, these capabilities support internal processes without affecting actuarial assumptions, methodologies or regulatory compliance.

Rather than replacing actuarial expertise, gen AI acts as an augmentative tool, extending actuarial capacity and improving agility. As its applications mature, gen AI is positioned to become a strategic companion—helping actuaries deliver faster, clearer insights and reinforcing their role as critical partners in insurance decision making.

Benefits and considerations

Adopting gen AI in actuarial work can bring significant benefits, but it also requires careful consideration of potential risks and limitations. Actuarial teams should weigh the following:

Key benefits

• Efficiency and productivity: Gen AI can handle the tedious and repetitive aspects of work (documentation, report drafting, data summarization) at high speed. This allows actuaries to complete projects faster and devote more time to analysis, review and decision support.
• Consistency and quality: By using gen AI to generate standard documentation and reports, firms can achieve a more consistent voice and structure in their work products. AI-generated drafts reduce the chance of human errors like omissions or inconsistent wording, with the ability to use best-practice language throughout.
• Enhanced insights: Gen AI’s ability to sift through large volumes of data and text can reveal patterns or connections that might be missed otherwise. For example, it can quickly summarize emerging trends from multiple experience studies or highlight anomalies for further investigation, thereby augmenting the actuary’s analytical capabilities.
• Talent empowerment: Leveraging gen AI can empower junior actuaries and analysts to produce high-quality drafts and analyses, accelerating their learning. At the same time, senior actuaries are freed up to focus on strategic thinking, client discussions and oversight, elevating their role as strategic advisors.

Key considerations

• Validation and accuracy: Gen AI may sometimes produce content that sounds correct but contains inaccuracies, known as hallucinations. Therefore, actuarial judgment and rigorous peer review remain essential. All AI-generated output—be it a report or an analysis—must be verified and validated by a qualified actuary. The tool is an assistant, not a replacement for expert review.
• Data privacy and security: Actuarial work often involves sensitive data (e.g., policyholder information, financial results). Using gen AI safely means ensuring no confidential data is inadvertently shared with external AI platforms. Organizations should implement secure, approved gen AI tools (possibly on-premises models or those with strong privacy safeguards) and clear guidelines on what data can be used with AI.
• Professional and regulatory compliance: Actuaries are bound by professional standards and operate in regulated environments. Any use of gen AI must comply with these obligations. That includes maintaining documentation of methods, disclosing material reliance on AI in reports if required and ensuring that AI usage does not violate any regulatory guidelines. Actuaries remain ultimately responsible for the work product and must exercise professional judgment in any AI-assisted work.
• Limitations and bias: Gen AI models, while powerful, do have limitations. They may not fully capture context or the unique aspects of a company’s business unless they are specialized or are trained on relevant data. There is also a risk of bias in outputs based on the training data of the AI. Actuarial teams should be aware of these limits, use domain-specific checks (e.g., does the draft assumption documentation actually reflect the company’s situation?) and treat AI recommendations with healthy skepticism when appropriate.
• Change management: Introducing gen AI into actuarial processes requires cultural adoption and training. Some staff may be unfamiliar or uncomfortable with AI tools. There may be a learning curve to developing effective prompts and interpreting AI outputs. Firms should plan for training, sandbox testing and gradual integration of AI into workflows to encourage the team to buy into the new tools and trust the process.
   

Implementation tips

Successfully integrating gen AI into actuarial work involves more than just adopting the technology—it requires strategy, governance and education. Below are best practices to help actuarial teams use gen AI safely and effectively:

Start with pilot projects

Begin by applying gen AI to a specific, manageable task such as drafting one type of report or summarizing a particular dataset. Pilot projects in noncritical areas allow the team to learn how the AI behaves and measure its impact. Early wins will build confidence and provide lessons before wider rollout.

Establish clear governance

Develop internal guidelines for AI usage within the actuarial team, leveraging an established governance approach such as the RSM AI Governance Framework. This process includes defining which tasks are appropriate for gen AI assistance, outlining required approvals or reviews for AI-generated content, and documenting the use of AI in actuarial workflow. Embedding principles such as accountability, explainability, privacy and regulatory compliance encourages AI to be used responsibly and ethically.

For instance, ownership of AI outputs should be clearly defined, data inputs must remain unbiased and content should meet professional actuarial standards. Clear governance not only protects data integrity and security but also promotes trust and consistency in actuarial deliverables.

Establish data security and privacy

Work closely with IT and data governance teams to choose the right gen AI solutions. If using third-party AI services, ensure they have robust privacy protections or use anonymized data. Consider on-premises or private cloud AI models for particularly sensitive work. Always err on the side of caution—when in doubt, don’t feed confidential information into any AI tool.

Train and upskill actuarial staff

Provide training sessions on how gen AI works and how to interact with it effectively (e.g., through crafting good prompts or questions). Educate the team about the tool’s limitations and the importance of verification. When actuaries understand AI’s strengths and weaknesses, they can use it more effectively. Encourage team members to share tips and use cases as they gain experience.

Maintain human oversight

Incorporate checkpoints in the workflow where human experts review and sign off on AI-assisted work. For example, if gen AI drafts an assumption memo, an experienced actuary should carefully review every section, cross-check figures and adjust language. Treat the AI as a junior assistant—valuable for efficiency, but always requiring supervision. This approach maintains quality and builds trust in the final outputs.

Iterate and refine

Collect feedback on AI’s contributions and continually refine its usage. Perhaps the AI tool’s first drafts need adjustments in certain areas—use that information to refine prompts or update the AI tool’s training data with company-specific context. Over time, the collaboration between actuaries and AI will improve, leading to better outcomes. Monitor key metrics like time saved, error rates and user satisfaction to gauge the impact and guide future enhancements.

By following these practices, actuarial teams can integrate gen AI tools in a controlled, effective manner—capturing the benefits while managing risks.

The takeaway

Gen AI is far more than a tech buzzword—it represents a meaningful evolution in how actuarial work gets done. By automating labor-intensive tasks and providing on-demand analytical support, gen AI allows actuaries to reimagine their role, focusing on value-added responsibilities.

Through real-world use cases and operational scenarios, it’s clear that gen AI can enhance consistency, streamline workflows and uncover insights that may otherwise remain hidden. The long-term vision is an actuarial function that operates with greater speed and insight: Routine documentation and number crunching are handled in seconds by AI assistants, while human actuaries focus on interpretation, strategy and advising leadership.

In essence, gen AI shifts actuaries from doing the work to directing the work—serving as a strategic enabler that amplifies human expertise. Importantly, this shift does not replace the need for actuarial judgment; rather, it enhances actuaries’ ability to deploy their judgment where it matters most.

Firms that thoughtfully embrace gen AI will likely gain a competitive edge, as their actuarial teams can respond faster, communicate more clearly and explore innovative solutions to risk and financial challenges. The actuarial profession has always been about marrying quantitative rigor with business insight—gen AI is a tool that, when used responsibly, can strengthen that marriage and expand the actuary’s impact in the insurance industry.

Is your actuarial team ready to leverage gen AI for a strategic advantage? Contact our team for a conversation about integrating gen AI into your actuarial operations. Our consulting advisors have experience helping insurance organizations implement AI-driven solutions in a prudent and value-focused way. Whether you are just exploring possibilities or looking to scale up your AI initiatives, we are here to provide guidance and support.