What AI can do in actuarial work right now

AI is already handling parts of what actuaries do — specifically the repetitive calculation and data-sorting work. Machine learning models can process thousands of insurance claims, spot patterns in historical data, and flag unusual cases faster than a person reading spreadsheets. Some insurance companies use AI to speed up the initial review of claims or to help price policies based on risk factors.

The key word is "parts". AI excels at finding patterns in data that already exists and doing the same task the same way every time. If you give an AI system ten years of claim data and tell it to predict next year's claims, it can do that. If you ask it to sort applications by risk level using the same rules every time, it will not get tired or make careless mistakes.

This has changed how actuaries spend their time. Fewer actuaries now sit at desks manually calculating tables. More of them now spend time deciding which questions to ask the AI, checking whether the AI's answers make sense, and explaining those answers to insurance companies and regulators.

Key Takeaways

  • AI handles data processing and pattern-finding in actuarial work, but actuaries still decide what questions to ask and whether the answers are trustworthy.
  • Actuaries make judgments about uncertainty and risk in situations that have never happened before — areas where AI cannot replace human reasoning.
  • Regulators require actuaries to sign off on insurance pricing and reserves, which means the role is legally protected even as the work changes.
  • The actuarial profession is shifting toward people who understand both statistics and how to work with AI systems, rather than people who do calculations by hand.

Why AI cannot replace the core of actuarial judgment

An actuary's main job is to estimate the cost of future events that have never happened before — or have not happened in the exact way they are about to. A pandemic changes how many people file life insurance claims. A new law changes how much insurers have to pay out. A climate shift changes which areas flood. An actuary has to estimate the cost of these things using incomplete information and professional judgment.

AI learns from patterns in historical data. It cannot learn from events that have not occurred yet or from situations that are genuinely new. If you ask an AI trained on ten years of flood claims to predict claims during a hundred-year flood, it will extrapolate from the ten years it knows — and it will be wrong. An actuary, by contrast, can reason about why this flood is different, what assumptions might hold and which might break, and what the cost could reasonably be.

This is why insurance regulators require a human actuary — specifically, a credentialed professional — to sign off on the numbers that determine how much an insurance company charges and how much money it sets aside for future claims. The regulator is not asking for a calculation. They are asking for a professional judgment about uncertainty. That judgment still requires a person.

Where actuarial jobs are actually changing

The shift is real, but it is not toward zero actuaries. It is toward fewer actuaries doing routine calculation work and more actuaries doing work that requires judgment, communication, and the ability to oversee AI systems.

Entry-level actuarial jobs that used to involve building spreadsheet models and running calculations are disappearing or shrinking. Companies now use AI or automated tools for those tasks. But mid-level and senior roles — the ones where actuaries design the models, decide what data to use, interpret the results, and explain them to executives and regulators — are not disappearing. They are becoming more important.

The profession is also expanding into new areas. Actuaries now work on climate risk, cybersecurity risk, and supply-chain risk — areas where historical data is sparse and judgment is everything. These roles exist because companies need someone who can think like an actuary: someone trained to estimate the cost of uncertainty.

What skills matter more now

If you are considering actuarial work, the technical skills that mattered most twenty years ago — the ability to do complex calculations by hand — matter less now. The skills that matter more are the ability to understand what an AI system is doing, to spot when its answers do not make sense, and to explain technical findings to people who are not mathematicians.

This means actuaries now need to understand programming and data science at a working level, not just mathematics. They also need to be able to communicate — to explain why a model's prediction is reasonable, or why it is not, in terms that a business executive or a regulator can follow. The best actuaries in a few years will be people who are comfortable with both the math and the tools, and who can think critically about what the tools are telling them.

How regulation protects the actuarial role

Insurance is one of the most heavily regulated industries in the United States. Every state has an insurance commissioner's office. Every insurance company has to file reports showing how much money it is setting aside for future claims. Those reports have to be signed by a credentialed actuary who takes legal responsibility for the numbers.

This regulatory requirement is not going away. In fact, as AI becomes more common in insurance, regulators are becoming more careful about requiring human oversight. Some states have already issued guidance saying that AI can information in actuarial work, but a human actuary has to review and approve the final numbers. This creates a floor under the demand for actuaries — companies cannot automate the role away because the law does not allow it.

The difference between "replacing" and "changing"

When people ask whether AI will replace actuaries, they usually mean: will there be fewer actuarial jobs? The answer is probably yes for certain types of jobs — the ones that are mostly calculation and data entry. But the answer is probably no for the profession as a whole, because the core of what actuaries do — making judgments about uncertainty and taking responsibility for those judgments — is not something AI can do alone.

The more accurate way to think about it is that the job is changing. The work that gets done by hand is shrinking. The work that requires judgment, communication, and oversight of AI systems is growing. Someone entering the field now should expect to spend more time working with AI tools and less time on manual calculation. But they should also expect that the profession will still exist, and that the skills they develop will be valuable.

Frequently Asked Questions

Can an insurance company use only AI and no actuaries?

No. Insurance regulators require a credentialed actuary to sign off on pricing and reserves. A company could use AI to do most of the work, but a human actuary has to review it and take legal responsibility for the numbers. The regulator is checking that a may have access to person made a professional judgment, not just that a calculation was done.

What happens to actuaries who only know how to do calculations?

Those roles are shrinking. Actuaries who want to stay in the field are learning to work with AI and data tools. Some are moving into new areas like climate risk or cybersecurity where judgment and communication matter more than routine calculation. Continuing education is becoming more important in the profession.

Is it still worth becoming an actuary?

Yes, if you are interested in uncertainty, risk, and mathematics. The job is changing, but the profession is not disappearing. The demand for people who can think about risk and explain it to others is growing, not shrinking. The path requires passing several exams and building technical skills, but those skills are valuable in insurance and in other fields.

What is the difference between an actuary and someone who uses AI for insurance pricing?

An actuary is a credentialed professional trained to make judgments about uncertainty and required by law to take responsibility for those judgments. Someone using AI for pricing is running a tool. The AI might do the calculation, but the actuary decides whether the calculation is reasonable, whether the assumptions are sound, and whether the answer should be trusted.