In Quentin Tarantino’s 1994 cult classic Pulp Fiction, when a situation deteriorates beyond control, everyone knows you call The Wolf. Arriving without the slightest hint of panic, he surveys the damage with practised calm and begins issuing instructions. Within minutes, chaos has been reduced to a sequence of manageable decisions. Insurance has long relied on people capable of performing a similar role. Actuaries are the professionals insurers turn to when risk becomes too complex for instinct alone.
When this challenge arises, actuarial models are put to the test to translate uncertainty into numbers, and those numbers into decisions about pricing, capital and commitments that may stretch decades into the future.
Now the tools used to perform that translation are changing due to the relentless surge of AI.
The scale of that technological shift is substantial. Research from McKinsey suggests artificial intelligence could generate as much as $1.1tn in annual value for the global insurance industry, driven by improved risk modelling, underwriting and operational efficiency.
But the story is not simply about new technology. It is also about how a profession built on statistical rigour is adapting to a world where the speed of decisions, and the stakes attached to them, are increasing.
“Automation is taking over repetitive, manual tasks,” says Mónica Carvajal-Pinto, Head of Actuarial Data Science – International at Parisian InsurTech Akur8.
Freed from those calculations, actuarial teams are beginning to spend more time examining what lies beneath the numbers.
“Actuaries can focus on deeper risk analysis, uncovering emerging trends and driving business strategy,” she says.
The shift, she argues, is fundamentally about perspective.
“The actuarial lens has widened,” she says. “It is moving from ‘how do we calculate this?’ to ‘what do these insights mean for our business, and how should we act on them?’”
Automation changes the work
The most immediate transformation in actuarial departments is happening quietly inside everyday workflows.
Historically, large parts of actuarial work involved running calculations, cleaning datasets and iterating models. Much of that process can now be handled automatically by modern analytics tools. The effect, therefore, is not to remove actuarial expertise, but to redirect it.
“With automation handling routine calculations, actuaries can dig much deeper into risk drivers and portfolio dynamics,” Carvajal-Pinto says.
That shift is becoming particularly important as insurers expand into more complex areas of risk.
“As products evolve to cover new and emerging risks, actuarial teams need the analytical capacity to understand what is driving those changes and what they mean for the business.”
In practice, that often means examining the broader forces shaping portfolios rather than simply validating the models that support them.
Higher stakes for insurance decisions
At the same time as this technological shift, the financial stakes attached to those decisions are growing.
Few markets illustrate this more clearly than retirement products.
Data from the Association of British Insurers shows that premiums paid into individual pension annuities reached £7.4bn in 2025, the highest level since pension freedoms were introduced more than a decade ago.
Yet the number of annuities sold fell slightly. What changed was the size of the pension pots being converted into guaranteed income. Sales of annuities above £250,000 rose sharply, while those above £500,000 increased by more than half.
In simple terms, fewer people are buying annuities, but when they do the financial decisions are significantly larger.
That has profound implications for insurers.
“These products last for decades,” says Yasser Rajwani, Product Manager at Earnix. “A small misjudgement around longevity, interest rates or policyholder behaviour can quietly compound over time.”
Modern analytics tools are helping insurers explore those uncertainties in ways that were previously difficult.
“AI-driven scenario modelling allows insurers to test thousands of combinations of assumptions very quickly,” Rajwani says.
But the technology does not eliminate the need for actuarial judgement.
“What is really changing is how actuarial teams contribute to the decisions those models inform,” he says. “They are increasingly part of the conversation about strategy, not just the mathematics behind it.”
A broader skillset
As the actuarial role evolves, so too does the range of skills required to perform it effectively.
Technical statistical expertise remains the foundation of the profession, but insurers increasingly expect actuaries to operate across a wider organisational landscape.
Communication, for example, is becoming just as important as modelling.
“Different teams within an insurer see different signals,” Carvajal-Pinto says.
Pricing and underwriting teams may notice shifts in customer behaviour or product demand first, while reserving teams may identify emerging claims patterns.
“When those perspectives are brought together, you begin to see the full picture of how risk is evolving.”
The ability to translate complex modelling outputs into insights that guide decisions across departments is therefore becoming central to actuarial work.
At the same time, actuaries are becoming more comfortable working alongside new technologies.
“Actuaries need fluency with automation, machine learning tools and modern data platforms,” Carvajal-Pinto says.
Understanding how those models operate, and where their limitations lie, is increasingly part of the job.
“Human expertise still needs to guide and sometimes override automated outputs.”
Keeping humans in the loop
The growing use of artificial intelligence across insurance has also raised new questions about governance and accountability.
Regulators expect insurers to explain how pricing decisions are made, document assumptions clearly, and demonstrate that models operate fairly. In that environment, actuarial oversight remains critical.
“The actuarial profession is undergoing a structural shift,” explains Hanre Cillie, Regional Vice President at nCino.
But the growing use of AI does not signal the end of the human role.
“We do not believe AI will replace actuaries,” Cillie says. “It is repositioning them.”
Machine learning and advanced analytics are already enabling more sophisticated predictive modelling and portfolio analysis.
But insurers still require judgement when interpreting those outputs.
“The real value of actuarial work lies in human assessment,” says Cillie.
That balance between analytical power and human oversight is becoming central to how insurers deploy new technologies.
Actuaries increasingly sit at the point where those forces meet.
Their role is no longer confined to building models. Instead, they are helping insurers interpret uncertainty, guide strategy and navigate the growing complexity of modern risk.
As artificial intelligence reshapes the tools available to insurers, the profession’s mathematical discipline, ethical standards and long-term perspective may prove more valuable than ever.
In an industry built on understanding the future, those qualities remain difficult to automate.
