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Actuary
Actuaries work with numbers to help organisations understand and plan for future risks. They work out the chances of things happening - like whether a car will be in an accident or when someone might claim on their insurance - and use those numbers to help companies make smart decisions about money.
Degree usually required
AI impact: medium£££ payUni route
52
AI impact
how much AI is reshaping it
Robin · your guide
Curious about being a actuary? Here's the honest picture - what you'd really do, what you'd earn, and every way in. No need to decide anything yet.
What you'd actually do
As an actuary, you are part mathematician, part detective. Companies and pension schemes collect mountains of data, and you work out what those numbers mean for their future. Insurance companies need to know: how likely is it that someone will have an accident? How much should we charge for car insurance? Pension schemes ask: will we have enough money to pay people later? You answer these questions using maths and statistics.
Your day involves building models - like a computer version of real life - that predict what might happen. You might spot that young drivers have more accidents, or that house prices affect insurance claims. You'll also explain your findings to people who don't know maths, so they can make good business decisions. You keep learning new software and new ways of working, because the world changes and your models need to keep up.
1Analyze statistical data to evaluate risks and financial implications for various scenarios.
2Develop and use mathematical models to predict future events and their financial outcomes.
3Prepare detailed reports and presentations to communicate findings to clients and stakeholders.
4Collaborate with underwriters, financial analysts, and other professionals to develop risk management strategies.
5Monitor and assess changes in laws and regulations affecting the insurance and finance sectors.
6Conduct regular reviews of existing policies and financial products to ensure they remain competitive and compliant.
7Utilize advanced software tools for data analysis and visualization to enhance decision-making processes.
Career progression & pay
01
Getting in
Junior Actuary
£28,000 - £35,000
BSc in Mathematics, Statistics, or a related field
In this entry-level role, you will assist senior actuaries in data analysis and model development, gaining practical experience while working towards your professional qualifications.
02
Building up
Mid-level Actuary
£45,000 - £60,000
3-5 years experience + completion of some actuarial exams
As a mid-level actuary, you will take on more complex projects, lead analyses, and begin to specialise in areas such as pensions or insurance.
03
At the top
Senior Actuary/Head of Actuarial Function
£70,000+
10+ years, chartered/fellow status with the IFoA
In this peak career role, you will oversee actuarial teams, drive strategic initiatives, and play a key role in shaping the organisation's risk management policies.
Degrees that lead here via Mathematical Sciences
Apprenticeships that lead here
Who hires - top UK employers
Aviva
A leading insurance provider in the UK, Aviva offers a dynamic environment for actuaries to thrive and develop their careers.
Lloyd's of London
As a global insurance market, Lloyd's provides actuaries with unique opportunities to work on complex risk assessments.
Aon
Aon is a global professional services firm that offers a variety of actuarial roles across different sectors.
Willis Towers Watson
This multinational company provides a range of actuarial services, making it an exciting place for actuaries to work.
Mercer
Mercer offers actuaries the chance to work on innovative solutions in health, wealth, and career sectors.
AI & the future of this job
Actuaries sit in a genuinely contested middle ground. The statistical modelling, data crunching, and routine report drafting that once consumed junior actuarial hours are increasingly handled by AI tools, which means the pipeline into the profession is narrowing at the bottom. However, the core of actuarial work, signing off on risk judgements that carry legal and financial weight, requires qualified human accountability that regulators and clients still demand. The profession is not shrinking so much as restructuring, with fewer entry-level roles but sustained demand for qualified fellows who can interpret, challenge, and own the outputs AI produces.
Within 5 Years
Workflow significantly compressed
By 2031, AI tools will handle the bulk of data wrangling, model parameterisation, and first-draft report generation that currently occupies graduate actuaries. Firms are already piloting LLM-assisted reserving and pricing tools. This will reduce headcount at the student and part-qualified level, making the early years of the career ladder more competitive and potentially slower. Qualified actuaries will spend more time on model governance, assumption setting, and client-facing interpretation, which demands stronger communication and commercial skills from day one.
Within 10 Years
Leaner, more senior-weighted profession
By 2036, the actuarial profession will likely look considerably smaller in total headcount but not in influence. Firms will run leaner teams of highly qualified actuaries overseeing AI-generated analysis at scale, particularly in general insurance pricing and pensions liability management. Specialists in areas where AI struggles, such as novel risk categories like cyber liability, pandemic modelling, and climate transition risk, will be in strong demand. The student-to-fellow dropout rate may increase as the early career experience becomes harder to navigate without a clear AI-augmentation strategy.
Within 20 Years
Redefined but durable role
By 2046, the actuarial role will have been substantially redefined around oversight, governance, and the translation of probabilistic AI outputs into accountable business decisions. Regulatory frameworks in the UK and EU are already moving toward requiring human sign-off on consequential algorithmic risk assessments, which structurally protects the qualified actuary's position. The profession may be smaller, more elite, and more interdisciplinary, blending actuarial science with data science and strategic advisory skills. Those who invest in the full qualification and build expertise in emerging risk domains will find the career genuinely resilient.
How to stay ahead
Prioritise Fellowship above all else
The IFoA Fellowship qualification is the single most important differentiator in this field. AI tools can replicate actuarial tasks but cannot hold actuarial credentials, and regulators increasingly require qualified sign-off. Treat every year of your degree as exam preparation, and choose employers who actively support study leave and exam funding.
Develop a specialism in emerging risk
Climate risk, cyber liability, longevity in the context of ageing demographics, and pandemic scenario modelling are areas where historical data is thin and AI models are weakest. Building deep expertise in one of these areas makes you the human that organisations need to challenge and contextualise AI outputs rather than simply accept them.
Learn to work with AI tooling, not around it
Understanding how actuarial AI tools are built, where their assumptions break down, and how to audit their outputs will be a core professional competency within five years. Take optional modules or self-study courses in machine learning and data science alongside your actuarial training, so you can credibly govern these systems rather than be displaced by them.
Build commercial and communication skills early
As routine analysis is automated, actuaries who can translate complex risk findings into clear business recommendations will command the strongest salaries and career progression. Seek roles and placements that put you in front of clients or board-level stakeholders early, and treat written and verbal communication as professional skills you actively develop, not soft extras.
How to get in - your routes
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