Actuarial transformation has been discussed for years, although progress has often been slower than the ambition behind it.
Many insurers have invested heavily in new modelling platforms and data improvements while still relying on manual reconciliations, fragmented processes and specialist knowledge held by a small number of people. Regulatory and accounting change has also absorbed significant actuarial capacity, leaving less time to redesign the function itself.
However, as technology improves, insurers are under pressure to get more value from specialist teams, and AI is now being implemented into practical workflows. The Institute and Faculty of Actuaries’ new AI Manifesto, published in September 2026, reflects that change, with the profession now considering predictive, generative and agentic AI alongside established actuarial approaches.
At the same time, insurers continue to modernise the systems and data used in actuarial work; Lloyd’s revised market strategy places renewed emphasis on process simplification, common data standards and incremental technology modernisation across the Market.
Now, the conditions for actuarial transformation look stronger than they have for some time, but the question remains as to whether firms are using this momentum to change how actuarial teams work in practice.
Systems modernisation is becoming harder to postpone
For a number of actuarial functions, legacy technology has been one of the biggest constraints on change, as models and processes have often developed over long periods, with manual adjustments, spreadsheets and local workarounds added as requirements changed.
While these systems may still produce the required output, maintaining them is becoming increasingly difficult as reporting expectations, data volumes and modelling demands increase.
Modern platforms now have the ability to improve runtime, scalability and integration with wider finance and risk architecture, while cloud infrastructure gives firms more flexibility to run complex analysis without relying on fixed local capacity. However, the value does not come from replacing one modelling platform with another.
In practice, migration creates an opportunity to review assumptions, simplify model architecture, remove redundant processes and address historical differences between actuarial, finance and risk data. If those issues are carried into the new environment, the organisation has modernised the technology while preserving many of the same operational constraints.
When firms are considering migration or model harmonisation, the case for change is now becoming broader than system support alone.
AI is moving into the workflow
AI is also beginning to change the practical nature of actuarial work. While early conversations focused heavily on what generative AI may be capable of, more recent activity is exploring how it can support specific parts of the actuarial process.
The Society of Actuaries has documented examples including data processing around models, generation of test cases and testing documentation, and model validation support.
The IFoA is also moving the conversation towards practical implementation. Its 2026 Artificial Intelligence and Actuarial Innovation competition included solutions designed to embed large language models into professional actuarial workflows with review, evidence and governance built around their use.
These examples highlight how there are parts of actuarial work where automation and AI can reduce the amount of time spent preparing information, reviewing routine outputs or producing supporting documentation, creating capacity for actuaries to spend more time understanding results and testing assumptions.
The opportunity may differ between firms, and the control requirements will vary between use cases, but in a function built around models, judgement and regulatory accountability, the ability to explain and validate output remains essential.
Data still detemines how quickly transformation can progress
Actuarial functions depend heavily on information produced elsewhere in the organisation, as policy, claims, finance, underwriting and external data all feed into modelling and analysis. When those inputs arrive inconsistently or require substantial adjustment before they can be used, the actuarial process slows down regardless of how sophisticated the model itself is.
Data transformation therefore needs to sit alongside actuarial transformation, including clearer ownership, more consistent definitions and stronger integration between the systems producing data and the models consuming it. It also means reducing the number of manual interventions required before an actuarial team can begin its analysis.
The current developments in market modernisation also make this increasingly relevant; Lloyd’s focus on common data standards and process simplification recognises that technology change depends on better information moving through the market.
For actuarial teams, improved data creates a practical benefit: less time reconciling inputs and more confidence in the information being used for reserving, capital, pricing and forecasting.
Governance needs to evolve with technology
More automation, cloud-based modelling and AI-assisted workflows introduce different questions around model ownership, validation, access, documentation and review.
The profession is already paying greater attention to those issues; in September 2026, the Society of Actuaries published practical guidance covering governance and ethics for generative AI models, reflecting the growing need to apply existing model-governance disciplines to newer technology.
Insurers therefore need to consider how their existing control frameworks apply as actuarial processes change; a faster model run has limited value if the result takes longer to validate. Automation does not improve the process if controls still depend on manual checks at the end, and AI-assisted work also needs clear accountability for the resulting analysis and decisions.
Transformation should make the control environment easier to understand and operate, particularly where actuarial outputs feed directly into financial reporting, capital management or regulatory submissions.
The actuarial operating model is also changing
As data preparation, repetitive modelling activity and parts of documentation become easier to automate, more value moves towards interpretation, challenge and communication, which in turn changes the skills required across the function.
Actuaries still need deep technical expertise, but modern teams increasingly need people who can work comfortably with data, technology and automated processes as well. They also need to understand how actuarial outputs connect with decisions being made elsewhere in finance, underwriting, risk and the wider business.
The IFoA’s current general insurance research programme includes working parties focused on actuarial strategy and development and the use of AI and machine learning in pricing, reflecting how closely technology and the future role of the actuary are now linked.
Transformation therefore has implications beyond efficiency, changing where actuarial expertise is used and how the function contributes to decision-making.
What would genuine acceleration look like?
The clearest evidence of actuarial transformation will be in the way the function operates; reporting and modelling cycles should become easier to manage, actuaries should spend less time preparing and reconciling information, and models should be easier to maintain, validate and adapt when requirements change, while senior actuarial resource should have more capacity to interpret results and support decisions across the business.
However, this requires a clear view of where the function is currently losing time and where technology could improve the process, which may involve system migration, model harmonisation, automation or better integration between actuarial, finance and risk. In many cases, several of those changes will need to happen together.
Actuarial transformation does appear to be accelerating, as the technology is more capable, the profession is engaging with it more directly and insurers have stronger reasons to address legacy processes that have been tolerated for years.
The firms that make the most progress now will be those that translate that activity into a better operating model, with stronger data, clearer processes and more time for actuarial judgement.
Brighter Consultancy works with insurers and reinsurers on actuarial transformation, including model harmonisation and re-tooling, technical solutions, platform selection, modelling and systems migration.
If you are reviewing how your actuarial function needs to change, speak to our team.
