Fabian Beiner created a website that classifies races that have the potential to be replaced by a robot. He calculated that actuaries have a 21% chance of fully automating themselves and that “they will almost certainly not be replaced by robots.” Analyze statistical data, such as mortality rates, accidents, illness, disability, and retirement, and create probability tables to forecast the risk and responsibility of paying future benefits. You can determine the required insurance rates and the cash reserves needed to ensure payment of future benefits. Now, let's move on to the job responsibilities of data scientists and actuaries to see how they differ.
So, with the exception of a few minor changes here and there, including automation to some extent, data science poses no significant threat to actuaries in the future. In addition, programming allows them to automate tasks using predictive models, which is far beyond the reach of actuaries. Instead, actuaries could spend more time analyzing and making recommendations in areas they understand well, such as marketing financial products and managing business risk. AI provides the actuarial profession with a structured, coherent and impartial way of performing actuarial work that minimizes the need for human intervention.
We're going to explore some direct differences between the two fields, which prevent data scientists from posing a threat to actuaries. Among all the other professions that data science has jeopardized with its evolutionary nature lately, actuaries have also begun to feel threatened. The objectives of actuaries are strictly limited to financial companies, but data science has no limits. This is because, despite automation, actuarial judgment continues to be applied at every step of the process, whether in the manipulation of data, the establishment of hypotheses or the selection of methodologies (mainly for the reservation of non-life insurance).
Although the roots of actuaries date back to the 17th century and, back then, they didn't rely on any automated way of processing data, the field has evolved a lot since then. Both fields are important and, although actuaries have a minimal scope, they are not easily replaceable simply because they have sufficient statistical skills. Actuarial sciences offer very specific training, such as FSA, ASA and CERA, allowing them to have extensive knowledge of all the statistical concepts required in their field. While a bachelor's degree is sufficient to become an actuary, the US BLS states that it takes 4 to 7 years for actuaries to obtain an associate level certification.
Actuaries will be freed from doing numerical calculations and producing reports, allowing them to spend time focusing on high-value activities, such as insightful recommendations, business development and risk management. Learning programming languages and learning about automation should be among the most important items on the actuarial priority list.
