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 predict the risk and responsibility of future benefit payments. You can determine the required insurance rates and the cash reserves needed to ensure the payment of future benefits.
Improvements, such as cloud computing, data warehousing and simplified actuarial software, have led to significant improvements. Future insurance success stories will rate the results of AI tools with good, outdated actuarial judgment, evaluating hypotheses and promoting appropriate checks and balances. 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). Not so long ago, when I first graduated, I calculated the numbers needed for actuarial reporting on MS-DOS programs, received data from CD-ROMs and saved the results in them.
The IBNR robot, developed by Nicholas Actuarial Solutions, has been implemented in real pricing and booking work. AI provides the actuarial profession with a structured, coherent and impartial way of performing actuarial work that minimizes the need for human intervention. Perhaps no role is as integral to the entire insurance operation as that of the actuary, and by understanding how AI will redefine the actuarial function, insurers will have greatly contributed to understanding the future of employment in the wider industry. Instead, actuaries could spend more time analyzing and making recommendations in areas they understand well, such as marketing financial products and managing business risk.
With the IBNR robot, data reliability is guaranteed, actuarial assumptions such as development factors, tail factors and initial loss rates are automatically selected, actuarial methodologies (paid versus natural language processing) and chatbots for resolving Claims to big data and algorithms in the actuarial back office, it seems that there is hardly a cog in the venerable insurance machine that AI will leave intact. The actuarial branch is especially prepared for the disruption of AI, in the sense that it has always focused on model creation, which is exactly the function that machine learning automates, iterates and optimizes. 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. With process automation and AI, actuaries would no longer waste their time needlessly processing data and calculating numbers.
However, instead of making decisions at every step of the process, actuaries only need to make decisions at the end to overwrite the model result when necessary. Since regulators, at least in personal terms, are concerned with avoiding unfair algorithmic discrimination, tomorrow's actuaries must not only understand but also effectively communicate how different risk characteristics affect client ratings. Much depends on insurers' AI investments, and business cases will be developed primarily by actuaries. .
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