Actuarial Sciences 1: Erik Haereid, M.Sc., on Actuarial Sciences and Actuaries (1)

Author(s): Scott Douglas Jacobsen

Publication (Outlet/Website): In-Sight: Independent Interview-Based Journal

Publication Date (yyyy/mm/dd): 2022/06/22

Abstract

Erik Haereid, born in 1963, grew up in Oslo, Norway. He studied mathematics, statistics and actuarial science at the University of Oslo in the 1980s and 90s, and is educated as an actuary. He has worked over thirty years as an actuary, in several insurance companies, as actuarial consultant, middle manager and broker. In addition, he has worked as an academic director (insurance) in a business school (BI). Now, he runs his own actuarial consulting company with two other actuaries. He is a former member of Mensa, and is a member of some high IQ societies (e.g., Olympiq, Glia, Generiq, VeNuS and WGD). He discusses: Actuarial Sciences; an actuary; the risks calculated by an actuary; a governmental or an individual basis; the requirements for becoming an actuary; the requirements for maintaining certification as an actuary; organizations; and the reputation of Actuarial Sciences.

Keywords: Actuarial Sciences, actuary, Erik Haereid, mathematics, statistics.

Actuarial Sciences 1: Erik Haereid, M.Sc., on Actuarial Sciences and Actuaries (1)

*Please see the references, footnotes, and citations, after the interview, respectively.*

Scott Douglas Jacobsen: You are the only person who I know with an expertise in Actuarial Sciences, except a distant family member, apparently, if I remember vaguely correctly. Anyhow, I reached out to do an educational series on this because I like working you. You’re knowledgeable and give solid responses to questions. You think about things. So, first session, is boiler plate stuff, defining terms in an accessible manner: What are Actuarial Sciences?

Erik Haereid[1],[2]*: As you say, I don’t like one or two sentence answers if I have more on my mind. Actuarial science could be defined by a few words, because the essence is mathematics and theoretical statistics on an M.Sc.-level, with additional education into insurance-related mathematics, relevant probability theories, some economics and finance theory, and computer science. The latter two is “new”; I didn’t study finance theories or computer science when I did this in the 1980’s. Before the 1980’s there were, at least in Norway, more economics and insurance business-related topics included in the education. The actuaries’ task or aim was not only to know about the fundamental math behind the many calculations of premiums and reserves, but also to manage to drive an insurance company as consultants and executives. Since this seemed to be a too big task for one education and profession, one focused educationally on the foundation of the insurance business; learn how to assess the right premiums and reserves.

I have to add that in many countries, actuarial sciences are also connected to the asset-side, creating statistical models that maximizes pension funds and other types of investments. Traditionally, and especially in my country Norway, actuarial science has primarily been about the liability-side of the business. Since actuarial science is about analyzing risks, actuaries are also used in other types of businesses than the insurance business, e.g., in general risk management.

So, actuarial science is primarily about insurance engineering. It’s the evolution of different mathematical methods used to create the best possible premiums and reserves. It’s also about stability; no one wants the premiums to deviate too much from a standard. It’s about trust. It’s about setting the premiums as right, i.e., low, as possible to meet the customers need. And it’s about sharing risks; dividing the insured into decent and political accepted groups, which both are acceptable for the people but also subject for optimal mathematical structures. E.g., it’s both political accepted and mathematical possible to divide cars into “expensive, new ones” and “not so expensive, old ones”, and people concerning life insurances into “people with low risk for death” and “people with high risk for death”. A 52 year old accepts that a 25 year old pays less for his life insurance. And because of enough data (experience) and good mathematical structures we can draw a life table with good estimates of probability for death, for each age.

The challenge has not only been finding the best mathematical methods, but to satisfy dramatic changes into certain risks (e.g., that people live much longer now than only a few decades ago) and establishing new risk factors where one so far has operated with assumptions (e.g., making interest rates stochastic within the insurance products).

For example, the old saving products (pensions, annuities and the like) contained some kind of death risk in the annuity. E.g., if you saved money to your pension, and died before you got some or all your savings, the insurance company kept the money or some of it (other saving products were the other way around; you got more than your savings if one died, and for that you paid a higher premium). This was a part of the product; in return the insured paid less premium. Most people didn’t accept this reverse insurance business, and wanted the bereaved to get exactly the savings if the insured died. But this is not insurance; this is bank without any economic risk if death. To label it “pension”, you have to include some kind of economic risk that you as an insured want to share with others. Then the insurance business constructed products that was close to bank savings, but had a small (but big enough for the authorities) internal risk factor that qualified them as “pensions” or the like; not a clean bank product.

If you don’t have any clue about the risk, you will for sure raise the premiums to an unacceptable level for the customers, avoiding bankruptcy. But then you don’t have a business; then people would create some sort of self-insurance. Insured events are in their nature random, or stochastic, which is a more common used word in probability theory, which is the basis of actuarial science. Its purpose is to find procedures for setting the optimal probability for an event you don’t know where, when and if will occur, and through that give it a value. Remember, insurance is usually (excludes annuities and saving products) about paying money which you hope you don’t get back.

Jacobsen: What is an actuary?

Haereid: An actuary is an insurance engineer; a person that have studied actuarial science and has some qualifications (usually nearby a Master of Science); an expert in building and use the mathematical framework to assess risks.

Actuaries are traditionally involved in the liability-side of the insurance business, ensuring that the single premiums and the total reserves are enough to fulfill the insurance unit’s obligations towards the insured. It’s basically two types of actuaries (two branches); actuaries that specializes in life insurance, annuities, pensions and so on (persons) and those whose discipline is casualty insurance (non-life).

My impression is that actuaries traditionally are more involved in the total insurance business in countries like UK and USA, than in Norway and many other countries, where specialization is more common. I think this has to do with the specific culture. In USA, the actuary profession is seen as one of the most important and desirable ones, while in Norway most people don’t know what an actuary is.

Jacobsen: What are the risks calculated by an actuary, often? Those most concerning or pertinent to the public with an interest in determining risk.

Haereid: There are different kinds of insurance-related risks, depending of which country you live in and what kind of insurance company you use. There are several risk classes and risk types, and one can read about these elsewhere. I will mention a few types, that may be of public interest.

Usually, the risks are as mentioned divided into two segments; life and non-life risks. Life risks, or person-related risks if you want, are typically death, disability, health-related risks, injuries, survival. Non-life risks are everything else; insured things or actions; property like buildings, vehicles, ships and so on, and actions like job-related mistakes (e.g., advices, consultant services, lawyers etc.) with economic consequences. A risk is linked to what kind of damage the life/thing is exposed to, the cost, and the probability behind that occurrence. Obviously, we always talk about a stochastic, uncertain future event. But the layman can use empirical data to say something about any such risk; you don’t have to use complex methods to say something about the risk for car damage or house fire. There is a lot of information on the Internet that would give everyone some ideas about risks. Life tables are probably possible to find and download (I haven’t checked) from different countries and segments of people (like men/women). Then you can say something about the risk part of the premium you pay to your life insurance.

E.g., risk as to car accidents and repair costs. There are several factors and aspects into account, like the model of the car (which steers parameters like how expensive the parts of the car are, and who drive that model (e.g., young risk-taking men drives certain types of cars; in my youth Golf GTI!), where the car is driven (in rural or urban areas), what it is used for (in business or to domestic use) and so on. As to buildings it’s risk factors like location (is it more or less danger for natural catastrophes like wind, water, avalanches and earth quakes), and fire (how are the buildings secured as to electricity and fire), costs (size, material, where and when and so on). You may also take into concern who lives there or uses it, how many and what type of use of the building and so on.

In insurances connected to one’s life, it’s relevant with risks like death, survival and health (e.g., disability). Life tables (death-probabilities) are usually divided into sex and age (risk classes); a woman has less probability dying than a man, and since it’s uncontroversial dividing premiums between men and woman, women pay less for their death insurance than men. The same with age; old people accept that they pay higher premiums for death benefits than young people. You could obviously divide the risks into more and smaller groups and classes, within decent statistical models, but of political and other reasons, one usually doesn’t. E.g., dividing into professions and lifestyles would be mathematically right (it’s clearly a statistical difference in risks for death (like it is for accidents and disability) between certain professions and lifestyles, as showed, e.g., in the movie Along Came Polly).

The risk I am most involved in is risk for survival. That’s the most obscure and amusing one, because it turns the business upside down. Normally you pay a premium in case of an unexpected event where you receive some money. Here you get a discount because the insurance company keep your savings in case of an event (death). It’s about annuities and pensions, and especially important as to lifelong payments (longevity insurances). People live longer, and this is a risk concerning pension payments. In Norway, in the insurance business, we strengthened the premiums and risk formulas in 2013, adapted to the fact that people live much longer now. The social security system “Folketrygden” (Norway) has gone through severe changes the last few decades, taking into account that people live longer.

In pensions related to employees and work, most companies (worldwide) go, and have gone from, Defined Benefit Pension plans (DBP) to Defined Contribution Pension plans (DCP); to ensure that the company (employer) has cash to fulfill their obligations towards the employees. As to pensions, it’s a huge challenge that we live much longer now than before.

Jacobsen: Are actuaries more often used on a governmental or an individual basis?

Haereid: Most on an individual basis.

Outside the private sector, actuaries are used in developing social security programs and pension schemes for the public, in institutions that supervises the insurance business, they are employed in special governmental institutions like the Financial Supervisory Authority of Norway (Finanstilsynet) and the Norwegian Public Service Fund (Statens Pensjonskasse). In UK you have institutions like the Government Actuary’s Department, and in USA the Social Security Administration, where actuaries are involved.

But most actuaries are employed in the insurance business; in insurance companies or as actuary consultants (as I am).

Jacobsen: What are the requirements for becoming an actuary, e.g., educational attainment/qualifications, formalized tests for certification, etc.?

Haereid: In my and some other countries the basic are mathematics, theoretical statistics (probability theory) and insurance-related mathematics on an M.Sc.-level (in some other countries you need less math and statistics (on a bachelor-level), but more diverse topics like computer science and finance-related mathematics and economics). In addition, there are some economics, financial economics and computer science. The education is comprehensive, and differs some between countries.

In Norway, the education is at universities. Before the 1980’s (when I studied), it was less math and probability theory, and more practical disciplines like economics and business administration. In my time, in the 1980’s, there was primarily mathematics, theoretical statistics and insurance-related mathematics. I have a M.Sc. in math/statistics from the University in Oslo. I didn’t know much about practical insurance before I learned it in my first jobs. But I knew something about how one created the insurance premiums and reserves.

Jacobsen: In Norway, and other countries if applicable, what are the requirements for maintaining certification as an actuary?

Haereid: There are some loose requirements about evolving educationally within topics like computer programming and finance mathematics, but one doesn’t lose one’s actuary title if one drops further education late in life and career; in Norway. (I am not sure about other countries’ practice.) One just loses work opportunities. Old actuaries, like me, fit into other parts of the actuarial realm. We know a lot, which younger actuaries don’t. We have some skills both as to our education and experience through a lot of years, that young actuaries need and don’t get through education or limited practice.

Jacobsen: In Norway, and other countries if applicable, what organizations coordinate, regulate, and standardize, the national and local actuaries, e.g., punish frauds, update community on standards, etc.?

Haereid: The local national actuary associations (e.g., The Norwegian Society of Actuaries; Den Norske Aktuarforening) make guidelines and standards that actuaries should follow. You also have global actuary umbrella associations, like AAE (the Actuarial Association of Europe) and IAA (the International Actuarial Association), which set global standards.

Beyond these there are some variations between countries as to standards, regulations, punishment procedures and so on. In Norway, the overall finance business is supervised by the Financial Supervisory Authority (Finanstilsynet). There are strict rules of what to do and not, including how the mathematical framework shall look like, and that the actuaries fulfill their obligations. E.g., in the early 1990’s I contributed to the mathematical groundwork for a new pension product in Norway, created by the insurance company I then worked for. It was based on old framework, but a lot of the structure was new. Then we had to get acceptance from the Financial Supervisory Authority to sell the new product with its mathematical framework.

If the political environment wants to change any laws concerning insurances, the actuaries are involved both as a consultative body (mainly through the national actuary association) and as contributors to mathematical structures.

Jacobsen: I’m told Actuarial Sciences are highly difficult. A lot of people can’t take the cognitive demands. Is this true? Whether so or not, why is this the reputation of Actuarial Sciences?

Haereid: You have to have the cognitive ability to understand mathematics and statistics up to a certain level (M.Sc.), but you don’t have to have any high IQ beyond that. If you have a dyscalculia but hold a 130 or 150 IQ, you can’t be an actuary, but maybe a genius in other areas.

The reputation is kind of a romantic perception; insurance is quite aware in most adult’s heads. People talk and think about it a lot. Everyone have ideas about sharing risk, and that there has to be some principles behind the procedures that evolves into what they pay. Because people know something about this, they tend to admire or respect even more those who knows this area fully. Maybe it’s something like that. It’s the same as when students look up to their professors, but the professors’ children don’t. It awakes the curiosity about what is on the other side of the mountains you see in front of you, but not about what you don’t see behind you. And because it’s quite difficult and one need time to evolve this kind of knowledge, and it’s not possible to explain in a simple way to the laymen, people tend to admire it even more. Another reason could be that most actuaries emphasize their theoretical background when they work and deal with ordinary employees and customers in the insurance realm, in the sense that actuaries seem like boring and dry human types, and that this is expressed by actuaries as an identification they get some positive from. Most actuaries are less boring and theoretical than most people think, but the actuaries themselves don’t want to reveal this “normal” trait!

Footnotes

[1] Member, World Genius Directory. Actuary.

[2] Individual Publication Date: June 22, 2022: http://www.in-sightpublishing.com/actuarial-sciences-1; Full Issue Publication Date: September 1, 2022: https://in-sightjournal.com/insight-issues/.

*High range testing (HRT) should be taken with honest skepticism grounded in the limited empirical development of the field at present, even in spite of honest and sincere efforts. If a higher general intelligence score, then the greater the variability in, and margin of error in, the general intelligence scores because of the greater rarity in the population.

License

In-Sight Publishing by Scott Douglas Jacobsen is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. Based on a work at www.in-sightpublishing.com.

Copyright

© Scott Douglas Jacobsen and In-Sight Publishing 2012-Present. Unauthorized use and/or duplication of this material without express and written permission from this site’s author and/or owner is strictly prohibited. Excerpts and links may be used, provided that full and clear credit is given to Scott Douglas Jacobsen and In-Sight Publishing with appropriate and specific direction to the original content. All interviewees and authors co-copyright their material and may disseminate for their independent purposes.