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How insurers can implement GenAI in insurance actuarial operations

Authors

  • Eric Wolfe

    Managing Director, Insurance and Actuarial Advisory Services, Ernst & Young LLP

  • Ben Yahr

    Managing Director, Risk Consulting, Ernst & Young LLP

  • Jennifer Haid

    EY Americas Insurance Actuarial Advisory Services Leader

07 minute read 18 Jun 2026

GenAI, richer data and intelligent operating models are empowering actuarial teams to deliver greater value across insurance.

In brief
  • Prioritize GenAI actuarial use cases (reporting, reserving/valuation, model modernization) to speed insight and cut cycle times. 
  • Scale GenAI with governed data, secure APIs and controls that keep outputs transparent, reviewable and auditable. 
  • Redesign operating models and upskill teams so actuaries orchestrate AI-enabled workflows and apply judgment where it matters most.

The technological advancements of the last several years have reshaped what’s possible for actuarial teams across the insurance industry. Generative AI (GenAI) and richer data sets are now in production at many insurers. As a result, actuaries are delivering more granular, actionable insights at greater speed. Questions that once took days or weeks to answer can now be addressed in hours or minutes. Many manual tasks have been reduced or eliminated. That shift frees actuaries to focus on strategic activities at a time when market volatility and intensifying competition demand faster decisions.

If you lead actuarial at an insurer, the mandate is to move faster while strengthening governance and decision confidence. This article outlines where GenAI is already delivering impact and the practical steps to scale it responsibly across data, controls, talent and operating model.

How EY can help

How does GenAI in insurance expand actuarial influence across the enterprise?

As GenAI scales across the organization, actuarial influence expands. Actuaries are increasingly positioned to: 

  • Influence strategic planning, capital allocation and enterprise-level decisions
  • Collaborate across product, finance, IT, risk, operations and distribution to embed actuarial insights directly into core workflows
  • Support innovation and personalization while helping the business respond to competitive pressure from new entrants
  • Foster a culture of collaboration, experimentation and innovation aligned to business objectives
  • Deliver high-quality outcomes while meeting operational and regulatory requirements

 

Where GenAI is already delivering actuarial impact in insurance

Model modernization with multi-agent GenAI

Model modernization is a prime example. Approaches featuring multi-agent GenAI can facilitate the migration of legacy actuarial code and validation against original outputs, helping teams update models more quickly while maintaining expectations for strong controls.

 

Now is the time for actuaries to assume these expanded roles. Advances in AI, combined with unprecedented volumes of accessible data, have put the necessary capabilities in reach for most insurers. Those that invest wisely and move decisively can boost productivity, strengthen competitiveness and capture first-mover advantage. In doing so, they elevate the actuarial role in the business and position the function as a central driver of value in AI-enabled enterprises.

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Technology and data are reshaping actuarial workflows

How are AI agents changing day-to-day actuarial workflows in insurance?

AI agents are becoming embedded in day-to-day actuarial workflows. The latest generation of AI is already being deployed across insurance organizations. Agent-based capabilities can execute multi-step tasks across tools and workflows and adapt as processes change. This represents a departure from earlier automation efforts. Rule-based robotic process automation struggled when data varied or processes evolved. Today’s AI applies reasoning to manage uncertainty and variation. As a result, it is more resilient and better suited to actuarial environments. 

 

In practice, AI agents function as specialized assistants embedded in day-to-day actuarial operations. They support repeatable activities such as data preparation, documentation assembly, standardized reporting, and elements of reserving and valuation workflows. Accountability remains with people, and actuaries retain ownership of decisions. 

Actuaries orchestrate AI-enabled workflows

As these tools become standard, actuarial work shifts further. Actuaries increasingly orchestrate work across human teams and AI-enabled workflows. They define objectives, sequence tasks, select tools and datasets. They review outputs and apply professional judgment, which ensures results are interpretable and decision-ready. AI-enabled workflows also connect processes end-to-end. They reduce handoffs and compress cycle times while preserving actuarial control over assumptions, governance and sign-off. 

Governance and controls for trusted AI-supported actuarial decisions 

Strong governance and controls are essential. As AI-generated insights are used more frequently in decision-making, trust becomes critical. Clear standards, transparency and accountability underpin that trust. Actuaries are well positioned to define standards of practice and shape governance models that allow leaders to rely on AI-supported insights. 

AI also reframes the business case for transformation, based on lower costs and higher rates of efficiency. But efficiency gains do not reduce the actuary’s importance; they reallocate capacity. Automating repeatable work shifts effort from process cycles toward more frequent scenario analysis, governance and enterprise decision support. When paired with redesigned processes and modern platforms, this shift delivers meaningful returns. Cloud-native infrastructure and elastic computing further enhance stress testing, forecasting and capital projections. 

The payoff, however, is not automatic. Data remains the foundation of actuarial judgment. Insurers need clean, governed and accessible data, supported by modern platforms and application programming interface (API) connectivity. Without this foundation, AI will not reach its full potential and returns will be constrained. 

Democratized access to trusted data helps actuaries move faster and reduces dependence on technical intermediaries. It is a prerequisite for capturing value at scale.

Strategic actions to accelerate technology and data transformation

  • Partner with business and functional leaders to identify high-impact AI use cases and build a sequenced roadmap.
  • Define standards of practice, governance models and control expectations that build confidence in AI-supported decisions.
  • Upgrade data pipelines, API connectivity and governance frameworks using modular approaches that evolve over time.
  • Design secure, governed self-service access to data.
  • Use open-source tools and cloud-native environments to increase transparency, flexibility, and speed.

Actuarial talent and skills for the GenAI era

How does GenAI in insurance change actuarial talent needs?

The role expands as execution becomes more automated. Reinvention places new demands on actuarial talent. This is not because actuaries lack capability today, but because the scale and scope of their contribution are growing. As actuarial work intersects more deeply with data, product, and finance decisions, actuaries are doing more of the work that differentiates the function. They apply judgment in uncertain situations, shaping the business questions that matter most and influence decisions that drive growth, profitability and resilience.

AI accelerates this shift. By reducing time spent on repeatable execution, actuaries have capacity for higher-value work, including strategic insight, collaboration and leadership. This combination brings technical rigor and business acumen together to elevate the quality of decisions across the enterprise. 

How AI is transforming actuarial roles 

Market dynamics increase demand for actuarial judgement across disciplines. Faster product development in response to new risks, new distribution models and non-traditional capital strategies are increasing demand for actuarial judgement across disciplines. Many actuaries are operating as multidisciplinary specialists, embedded in innovation programs and playing larger roles in product lifecycle management, FP&A, reinsurance and asset-liability management. 

Will AI replace actuaries? The value shifts toward judgement and accountability

There has been speculation about AI replacing actuaries. Advanced technology matters, but talent and leadership determine whether AI translates into real business value. By automating repeatable execution, AI actually increases the importance of judgment, governance and accountable decision-making. In other words, high-performing actuarial teams remain the bridge between the potential of advanced analytics and the impactful actions that will lead to value for the business. 

Strategic actions to accelerate talent transformation 

  • Define future actuarial skill profiles and recruit, train and reskill accordingly.
  • Build upskilling programs focused on AI fluency, analytics and business influence.
  • Create adaptive career pathways that develop leaders, connectors and technical specialists.
  • Optimize engagement and location strategies to support collaboration across distributed teams.

Operating models that scale AI-enabled actuarial performance

What operating models can scale AI-enabled actuarial performance?

Realizing the full potential of AI-enabled actuarial transformation requires more than new tools or incremental reskilling. It requires a deliberate operating model designed for speed, integration and accountability. When paired with refreshed talent and modern technology, a strong operating model amplifies performance gains.

Operating models define how organizations structure processes, deploy talent and use data and technology to make decisions. They clarify roles and responsibilities, establish governance and enable collaboration across functions.

There is no single best-practice template. Effective operating models reflect organizational strategy, culture and maturity. Still, the most successful transformations consistently share five elements.

Five elements of operating models that work 

Iteration should be treated as resilience, not rework. As technologies and market demands shift, operating models must evolve deliberately – guided by metrics, decision quality and business outcomes. 

Strategic actions to accelerate operating model transformation

  • Distinguish where best-in-class actuarial judgment is required versus those suitable for automated execution.
  • Support change with strong adoption and change-management discipline.
  • Use creative sourcing options where scale or specialization is needed.
  • Select partners based on skills, data access, technology and cultural fit.

Unlocking the full strategic potential of the actuarial function

AI’s most profound impact may be in unlocking the full strategic potential of the actuarial function. AI accelerates insight generation and strengthens judgment in the face of uncertainty. It embeds analytics more directly into everyday decisions. Together, these changes allow actuaries to move beyond execution and fully step into their role as value creators. 

Technology alone is not enough. Insurers that capture the most value modernize talent, operating models and technology together. They align judgment, governance and accountability with AI-enabled capabilities. Those that act decisively will strengthen decision quality and improve performance across the enterprise. In an increasingly complex and volatile environment, this integrated approach can become a durable source of competitive advantage.

The following individuals also contributed to this article: 

  • Sherry Chan, Managing Director, Ernst & Young LLP
  • Daniel Jonas, Manager, Ernst & Young LLP

Summary 

AI is fundamentally reshaping actuarial work by accelerating insight generation and reducing time spent on repeatable tasks. This shift allows actuaries to focus more on judgment, governance and enterprise decision support. Insurers that modernize data, technology, talent and operating models together can amplify these gains, strengthen trust in AI-supported insights and position actuarial teams as central drivers of value in an increasingly complex environment.

About this article

Authors

  • Eric Wolfe
    Managing Director, Insurance and Actuarial Advisory Services, Ernst & Young LLP
  • Ben Yahr
    Managing Director, Risk Consulting, Ernst & Young LLP
  • Jennifer Haid
    EY Americas Insurance Actuarial Advisory Services Leader
Related topics
Financial services
Insurance
AI
Technology

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