Professor Schmidt, ongoing digitization is having an impact on every industry. How does it affect the day-to-day work of actuaries in particular?
Nowadays, actuaries are no longer only found in “traditional” insurance companies. They are also to be found among the many consulting, auditing and service companies in the industry that offer services associated with the traditional insurance business. The scope of topics covered by actuaries has also expanded significantly since the 1990s. While they were originally tasked with working on cash flow and reserve calculations, today they are involved in a wide range of tasks, including risk management, capital investment and IT. A “typical actuary’s working day”, which may have existed 30 years ago, definitely does not exist today. Depending on the field in which they work, an actuary today also has to specialize in certain areas, e.g., risk management or product development.
However, what continues to characterize all actuaries are strong analytical skills, the ability to analyze complex issues quantitatively, and a critical approach to data and calculation results. They are true geniuses when it comes to numbers and data. This explains why actuaries are held in such high esteem in the insurance industry – and why they are so important for the success of a company!
When it comes to data and analytics, AI and automation can already handle many tasks better than humans. Will this make actuaries superfluous in the future?
Absolutely not! If I were to make a prediction, I would say the opposite. Indeed, AI and automation will further strengthen the role of actuaries in the insurance industry and tend to expand their areas of responsibility. AI and automation are currently proving successful in the insurance industry in those areas where there is a large amount of data that is not subject to any major changes over time. If we think of automatic text recognition, for example, the successes there are so impressive, among other reasons, because our characters do not change continuously. Insurance claims, on the other hand, can be influenced by ongoing changes in the prevailing conditions and environment; collision claims in motor insurance, for example, are determined by new safety systems in road traffic. This is a very different environment than it was 20 years ago. And let’s not forget digitization: everyone is currently talking about cyber insurance. In some cases, however, there is no data at all on past risks, which makes risk assessment very challenging and fraught with uncertainty. Consequently, risk assessment by machine is only ever possible to a limited extent. In my view, “actuarial intelligence” is required here, for instance to deal with uncertainty or a lack of adequate data. That being said, AI and automation can greatly assist actuaries in their work and improve quality. They will not replace people, however.
Insurance companies must take numerous regulatory requirements into account. To what extent does this affect the adoption and ongoing development of technologies such as AI?
Here we have to distinguish between the different segments of the insurance industry. In private health insurance, lawmakers impose the most requirements on the calculation in comparison with the other segments. Actuaries are bound by strict rules in this respect. It is difficult to judge whether modern processes would add value for customers and companies. From today’s perspective, I would doubt this, as the prevailing conditions change regularly with regard to claims for damages and the uncertain data situation makes it more difficult for AI and automation to be successful in costing.
In life insurance, the situation is somewhat different. From a regulatory point of view, actuaries must pay particular attention to the prudence concept when calculating premiums. This requires certain actuarial assumptions and therefore also an appropriate data basis. This may well be available, in which case AI can then provide good support at various points.
In indemnity/casualty insurance, on the other hand, the regulatory requirements are comparatively manageable. New approaches and technologies can therefore be tried out and tested more quickly. This is where I currently see the greatest potential for the use of artificial intelligence in typical actuarial issues.
I also expect AI methods to add a great deal of value – irrespective of the insurance line in question – when it comes to optimizing business processes. There are, for example, promising approaches for AI-based claims settlement or benefit verification.
Is there still a demand for actuarial training when data, AI and analytics play such a big role? Shouldn’t students focus on data science instead?
Actuarial training in Germany is very well organized, both in universities and in the German Actuarial Association (DAV) and meets the requirements of the International Actuarial Association. At the same time, actuarial training also includes skills that typically fit the profile of a data scientist. For students who are currently faced with a choice between actuarial science and data science, I therefore recommend the DAV training. At the same time, however, I would also encourage individuals to improve and strengthen their data science skills, as they will become increasingly important in many actuarial activities in the future. It is also possible to obtain an additional qualification as a “Certified Actuarial Data Scientist” (CADS) from the DAV. Ultimately, every student should be comfortable with their own decision and enthusiastic about pursuing their goal. In my view, both professional profiles offer excellent prospects in the job market since the insurance industry has need of both actuaries and data scientists. Both profiles are also in demand at consulting companies such as Comma Soft, which support insurance companies with their digital transformations. All in all, there is considerable demand for interdisciplinary skills. This is what makes actuarial work and consulting in the insurance sector so exciting and varied.
Prof. Dr. Jan-Philipp Schmidt is a business mathematician and mathematician. He has been a Professor of Actuarial Science at the Cologne University of Applied Sciences (TH Köln) since 2016, where he teaches in the Bachelor’s and Master’s degree programs in Risk and Insurance. Prior to that, he worked as an Actuarial Management Consultant at the ifa Institute in Ulm, among other positions. Through his expertise in research and business, Professor Schmidt supports the insurance industry as Vice Chairman on the Board of the German Society for Insurance and Financial Mathematics (DGVFM) and as a member of the German Actuarial Association (DAV).