actuaries vs data scientists

It is not often someone probes the debate “Doctors vs Taxi drivers” or “Hairdressers vs Astronauts”, which leads me to wonder why the debate of “Actuaries vs Data Scientists” is appearing so regularly, as of late, within the finance world.

The answer, I believe, is because the discussion is quite an unsettled one, and not entirely obvious. In fact, anyone who is not an expert in both fields will struggle to determine differences between these two disciplines. Some people may even think these terms are interchangeable. Actuaries were deemed to be the world's original data scientists after all!

As an Actuarial Science and Risk Management student, who is on the brink of graduation, I am determined to establish a reasonable and educational response to the 'Actuaries vs Data Scientists' debate, as this may be the very article which determines the career path I follow after throwing my academic cap in the air. 

I was guided into pursuing an Actuarial Science degree at Queens University under the advice of my secondary school career’s guidance officer. He informed me it was a prestigious degree, which pays very well, and was perfectly suited to my mathematical capabilities. Check, check and check!

Of course, I then embarked on my own research into actuarial science which sparked my interest in the field, attending taster days and trying different work experiences. However, I don’t think it was until my third year of my actuarial university studies that I realized what exactly actuaries do. Even now (and I am sure many actuaries can agree with me), I am always hesitant to answer that question, ‘So what is an Actuary?’ at a dinner party. Which makes me wonder, do data scientists feel the same way?

Consider the definition which the Institute and Faculty of Actuaries (IFoA) offer;

Actuaries are problem solvers and strategic thinkers, who use their mathematical skills to help measure the probability and risk of future events. They use these skills to predict the financial impact of these events on a business and their clients.”

Could this definition also describe ‘what is a Data Scientist?’ Personally, I think it could, but according to Wikipedia data science is:

An inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data and apply knowledge and actionable insights from data across a broad range of application domains.”

These two definitions, although worded quite differently, are describing suspiciously similar roles.

Which begs the question, why do we even need to know what the differences are in the Actuaries vs Data Scientists debate? Who is actually concerned?

For the general public, I can reasonably predict that this question will rarely pop-up in conversation. Those who are most likely to be concerned with the differences between the two are in fact actuaries and data scientists themselves, or students who are trying to choose which career to pursue. For this reason, it is necessary to dive into the details of what actuaries vs data scientists have in common, and probably more useful, what makes the two different.

Actuaries Vs Data Scientists: Background & Training

There have been a number of claims made for the title of the creator of the first ever ‘mortality table’ (thus the first actuary), dating as far back as Ulpian, a Roman Juror, in the third century. However, John Graunt or Johan De Witt are more commonly awarded the title, for tables they published in the 1660s and 1670s respectively. Clearly, the actuarial profession has been around for quite some time.

Since then, the title has evolved into a “globally recognized profession”, which requires the completion of a well-defined pathway of actuarial examinations. These actuarial exams are regulated by mutual recognition agreements between the IFoA and other actuarial associations.

There are several different ways to become an actuary:

  • Complete a degree in Actuarial Science, obtaining some exemptions from the professional exams, which can then be completed in the workplace.
  • Start with any maths-based degree at 2:1 level or above and begin taking actuarial exams once they having secured a graduate role (13 exams in total).
  • Obtain an actuarial apprenticeship.
  • Complete all the exams independently.

Consequently, claiming the title of an “Actuarial Professional’ is no easy task, and takes many years of hard work. However, I have been assured that the pay-off is quite worth the wait.

Data Science, however, is a relatively new career, one that I wasn’t even aware of when I was applying for university only 4 years ago. Remarkably, it is now one of the most popular job titles, with the number of roles for Data Scientists having grown by around 650% since 2012. About 11.5 million jobs will be created by 2026 according to the U.S Bureau of Labor Statistics’.

One can label oneself as a ‘Data Scientist’ with a lot more ease than that of an ‘Actuary’. This is because the emerging career has not undergone the ‘professionalizing’ that the rigorous actuarial profession has.

Then how does one become a Data Scientist? When googling this question, I am greeted with a tiresomely long list of open online courses, mini qualifications and advertisements for apprenticeships.

According to some websites, I technically already am a data scientist, as I have the skill set necessary for the job; mathematically and statistically minded, computer language proficient etc. (this will be discussed later). As the former President of the IFoA, ‘John Taylor’ agreed

Actuaries… [can be] described as the original data scientist.

So, if I technically already am a data scientist, then is becoming a data scientist too easy? I am a Data Scientist already without even realising it? This dilemma is very well addressed in an article written by Maurice Ewing, who has trained and led data science teams across 50 different countries. His answer is

No, data science is not easy. It's just unshaped and not “professionalized.”

He uses the analogy that you cannot simply become a lawyer by watching daytime
courtroom TV. Law, similar to Actuary, has been professionalised, thus both can safeguard their gates, choosing who labels themselves as a Lawyer or an Actuary. Data scientists cannot do this yet. It seems that anyone can label themselves as a data scientist. However, Ewing suggests:

Data science excellence requires a number of years in actually applying it before one can truly understand data, how it behaves, how different models work, backwards and forwards, etc. Yet, most importantly, excellence requires making mistakes and understanding mistakes, along with appreciating the variations between observed and predicted reality. Thus, I affectionately call data science the science for imperfect people, like myself.”

I particularly love this quote, as it shows that you can label yourself as data scientist, but that does not mean you are an expert and certainty not a professional in this field. This does not suggest there are no experts and professionals within the field, but that they are just slightly harder to identify.

Now that the means of entry into these two fields has been established, one wonders what is it that these professionals do?

Actuaries Vs Data Scientists: Responsibilities & Skillsets

From our definitions of Actuaries vs Data Scientists, we can see that both roles use data to try and predict future events. Generally, an actuary’s role within a company is to analyze the statistical data they are given, e.g., accident rates, or insurance claims. They then use computer modelling, usually done on excel, or other more advanced software, to determine any potential risks. If necessary, they will prepare presentations and reports, communicating findings to co-workers, clients or stakeholders.

Data scientists similarly cannot seem to get enough data. They use an almost identical skill set to actuaries, with the same techniques including Statistical analysis, Mathematics, Programming, Data Visualization and of course Communication. However, data scientists are usually more involved in acquiring, processing and cleaning the data (commonly referred to as ‘data mining, cleaning and munging’). A data scientist should also have a comprehensive understanding of machine learning algorithms (Artificial Intelligence). Due to this wider, less specified skillset, data scientist are to be found in virtually any company. Hence, leading on to further differentiation; The Problem Set.

Actuaries vs Data Scientists: Data Science skills

Actuaries vs Data Scientists: The Problem Set

What is the 'problem set' for Actuaries vs Data Scientists?

Data scientists tend to work with ‘big, unstructured data’, while actuaries specialise in ‘structured data’. This can be seen as one of the biggest differences between the two roles. The type of data one is working with is determined by the field one is working in. Actuaries are typically found in traditional fields such as life, pensions and insurance, where they are commonly forecasting the probability of loss, calculating how the loss will affect the insurer, and estimating what the insurer needs to charge as a premium in order to make a profit.

Data scientists, however, can be located in essentially any field there is data. The fundamental role of data science is useful in every single company, this is why it is currently recognised as the

Reigning technology that has conquered industries around the world... [Bringing]… about a fourth industrial revolution.”

It has been said that:

Data has become the fuel of industries. It is the new electricity. Companies require data to function, grow and improve their businesses.”

Hence, data scientists can be found in any company ranging from IBM (2,563 data workers) and Amazon (1,846 data workers), to any new small start-up or tech firm. It is common for a data scientist to work as a consultant, contributing to important decision-making procedures and supporting companies make smarter, more efficient business decisions.

After considering just a few of the similarities and differences between these roles, I start to understand John Taylors message that;

Data science is very empowering for actuaries.”

Actuaries have somewhat paved the fundamentals for data science, however, it is data scientists who have glamourized and skyrocketed what actuaries were doing day-to-day. The emergence of AI, blockchain, autonomous vehicles, predictive marketing and even your recommended playlist on Spotify all have been pioneered by a data scientist. It is no wonder that data science has been pinned as

If data scientists are talented enough to predict my next purchase, even before I know what I want/need myself, then surely, they have the capabilities of making us believe that they have the most attractive job on the market. Of course, this is just an actuary’s concern and speculation (or maybe jealously).

Nevertheless, instead of Actuaries vs Data Scientists, I believe it is vital that both disciplines work together, sharing insights and knowledge to try and help each other create a new working environment, where they utilise all the data available to create an efficient, safer era.

It is exciting to see the IFoA launching a ‘certificate in data science’ which will offer a set of five modules that covers “disciplines such as data visualisation, Artificial Intelligence and Machine Learning.” This is the beginning of the new generation of actuaries. Most duties being carried out by actuaries will soon be digitalised, but this does mean our field is ‘dying out’ but instead evolving. Actuaries will simply have more time to perfect and hone other skills and find new, possibly more important tasks to complete relative to the future marketplace.

Finally, as actuaries, I personally feel, we must be cautious that we do not ‘over- professionalise’ data science. Maybe the reason that is has taken off so exponentially, is due to its freedom and non-specification. If we create a pre-determined curriculum, whereby professional exams have to be undertaken to gain the title ‘Data Scientist’, (similar to what actuarial science currently is), will they again face the threat of being left behind in the next revolution? Possibly the beauty of this new age is the scope for creativity and inventiveness.

In two months, I will enter the chaotic pursuit for a job in the financial world. I am determined to successfully complete my actuarial exams (quite possibility because I have the mind-set that I have started them so must finish them). But I am now aware of the opportunities that exist beyond this profession, which motivates me to continuously advance my skill set to keep up with the advancing world, and become part of the new generation of hybrid actuaries. As Anthony Iannarino once said,

Focus is the new currency of success.”

“This is a guest article written by Maeve McCullagh. Maeve can be found on LinkedIn here.”

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