Actuaries

Actuarial work already uses a lot of computer power. New AI tools can speed up the data and math side of the job. For example, AI can help check big spreadsheets, write the right formulas, and summarize data ^[1].

Some insurance companies are investing in AI to automate pricing and risk modeling, which helps set premiums faster ^[2]. But complete replacement of human input hasn’t happened. Experts note that AI “enhance[s] pricing and underwriting” but full removal of people “has not been successful” ^[2].

In practice, AI usually augments actuaries: it suggests models and calculations, but actuaries still interpret the results. Tasks like explaining policy or testifying in court rely on human knowledge and communication skills ^{[2] [3]}. As one industry study puts it, AI should be “a complement to, rather than a replacement for” the judgment and expertise of actuaries ^[3].

Practically, this means routine analyses can be done faster, but actuaries remain in charge of checking models and making decisions.

Insurance firms are interested in AI but moving carefully. On the plus side, many AI tools (like big data analytics and chatbots) are now available, and actuaries command high pay ^[4], so companies see potential cost savings in automation. However, insurers see big hurdles.

A recent poll found the main barriers are not enough skilled people who know both actuarial science and AI, and worries about accuracy and trust ^{[5] [6]}. Regulators also require clear, explainable methods for pricing, so firms must be sure any AI model can be explained and audited. As a result, adoption is fairly slow: many companies are still experimenting with AI on simple tasks (like claims sorting), rather than handing critical decisions over to a computer ^{[6] [7]}.

In short, actuaries are gradually learning to work with AI. If done well, AI can take over repetitive parts of the job, but actuaries’ training, judgement, and ethics will still guide the final decisions ^{[7] [3]}.