Artificial Intelligence (AI) is becoming omnipresent, gradually reshaping our world. In the realm of insurance, Actuarial science—traditionally reliant on mathematical and statistical methods to evaluate and manage risk—is undergoing a significant transformation thanks to AI.

The Limitations of Traditional Actuarial Science

Historically, Actuarial science has depended on statistical models and historical data to assess risk and determine premiums. However, the surge in data volume in recent years has made this process increasingly complex. This is where AI steps in, exemplified by platforms like ADA.

Introducing ADA

ADA, our Artificial Digital Actuary, revolutionises the Actuarial process by rapidly and accurately processing vast amounts of data. This enables us to offer Managing General Agents (MGAs) a platform with dashboards that provide granular, near-real-time insights—capabilities previously unattainable due to the complexities involved.

ADA automates routine tasks such as data cleansing and processing, freeing up Actuaries to focus on more intricate tasks like developing new risk models and analysing emerging risks.

The Future with Predictive Models

AI and data science are further transforming Actuarial science through the development of predictive models that analyse diverse data sets, including demographic information, claims data, and economic indicators. These models can predict future risks and identify potential losses, enabling insurers to make more precise pricing decisions and enhance their underwriting processes.

The future of Actuarial science is inextricably linked to the ongoing advancement of AI platforms like ADA. As data volumes continue to grow, the importance of AI and data science in Actuarial science will only increase. MGAs who embrace these technologies and adapt to the evolving landscape will be well-positioned for future success.

The Impact of Technological Progress on Employment

The effect of technological progress on employment varies by profession and innovation. Some inventions, like the gasoline-powered tractor, drastically reduced farm-related jobs in the early 1900s but boosted manufacturing employment.

Other innovations, such as improvements in computing speed and portability, have gradually expanded Actuaries’ toolkits without significantly reducing Actuarial jobs. This resilience supports the belief that cognitively challenging work is relatively robust to disruption.

However, the rapid emergence of generative AI tools like ChatGPT, which gained 100 million monthly active users shortly after its release, has professionals previously seen as disruption-proof worried about their future job prospects.

Speculative Scenarios for Actuaries

Predicting the future of employment is challenging, but Actuaries, skilled in estimating uncertain futures, can envision and prepare for their own futures in an AI-defined job landscape. Here are four speculative scenarios:

Scenario 1 — Doomsday

The most unsettling scenario is the potential obsolescence of Actuaries in their current form. Researchers estimate a 50% chance that within 120 years, all occupations could be fully automatable, with significant job reductions occurring sooner. A 2013 study estimated a 21% probability of Actuarial jobs being automated within the next decade or two, while a more recent study increased this estimate to 52%.

These estimates suggest that Actuarial work may not be as cognitively dynamic as presumed. Routine tasks, such as periodic rate reviews or predictive model refreshes, may be highly automatable. Even managerial roles, where Actuaries could oversee teams of AI bots rather than people, might not be immune to automation.

AI’s ability to approximate creativity poses a further challenge. While algorithms may not produce genuine novelty, they can expedite discovery, offsetting some value lost by forgoing occasional genuine breakthroughs.

Scenario 2 — Groundhog Day

A more status quo scenario envisions the nature and number of Actuarial jobs remaining stable. Even if AI offers a cheaper, comparably effective alternative to humans, employers may be reluctant to fully embrace it. For instance, despite human errors causing many vehicle collisions, nearly half of U.S. adults surveyed felt widespread use of automated vehicles would be detrimental to society. Similar reluctance could slow AI’s adoption in Actuarial work.

Companies are also not categorically opposed to long-term investments in people, even at the potential short-term expense of productivity. Many organisations support Actuarial study programs, indicating a continued investment in human development. Even if AI eliminates routine tasks, Actuaries could focus on higher-quality, non-routine work, maintaining demand for their expertise.

Scenario 3 — Training Day

In this scenario, Actuaries transform into roles more akin to data scientists. AI tools can produce inaccurate results due to various factors, creating high-paying opportunities for “prompt engineers” to extract higher quality responses. Asking good questions about complex risk dynamics will require Actuarial skills.

AI’s current limitations in handling domain nuance, exemplified by its failure in advanced Actuarial exams, suggest that Actuaries will play a crucial role in training AI. Actuaries could develop domain-specific models and plug-ins, enhancing AI’s effectiveness. This continuous training would leverage Actuarial expertise while requiring more profound data science skills.

Scenario 4 — Judgement Day

In this scenario, Actuaries pivot towards social sciences to manage AI risks. As AI can memorialise hidden biases in data, Actuaries will play a vital role in ensuring ethical and accurate modelling. Regulators may enhance their teams’ abilities to review complex algorithms, creating new roles for Actuaries. Diverse modelling and review teams will be essential to address prediction errors correlated with demographic groups.

Actuaries with social science expertise will unpack the “why” behind data, ensuring AI does not rush to judgement. This human touch will be critical in managing AI’s complexities.

Creating the Future

The future of Actuarial science will likely combine elements of all four scenarios. Actuaries can influence this future by focusing on what they can control, creating better outcomes for their stakeholders. Just as writers sought to regulate AI’s use in content creation, Actuaries can ensure their relevance by continuously adding value and leveraging their unique skills.

Ultimately, Actuaries who embrace AI’s potential, adapt to new roles, and maintain their focus on stakeholder value will thrive in an AI-driven future.