To be or to be both: Training actuaries as data scientists

To be or to be both: Training actuaries as data scientists

Guardian Life recently announced it is training its in-house actuaries to double as data scientists.

“We made an investment in General Assembly, and they’re training some of our actuaries to become data scientists. We think there is a natural opportunity there as they’re both familiar with using algorithms,” Guardian Life CIO Dean Del Vecchio told Forbes.

They’re not the only ones doing this, though they’re one of the first to openly talk about it.

Right now you might be thinking, “Huh...seems a bit like overkill.” Turns out there’s a number of potential benefits to this approach.

Actuaries vs. Data Scientists: The Final Showdown (just kidding)

Actuaries represent the pinnacle of insurance industry expertise. Their field of study and knowledge is both deep and narrow, a bit like a well. Data scientists, on the other hand, typically have a much broader range of knowledge, but lack the depth of expertise, more like a pool.

Becoming an actuary is similar to becoming a lawyer, doctor, or CPA. There’s a definitive educational process culminating in the actuarial exams and the professional designation of actuary (n): an insurance professional who compiles and analyses statistics in order to calculate insurance risks and premiums.  

Data science (DS) is the process of using scientific methods, approaches, and systems to extract information from large quantities of data. The data science profession encompasses a wide range of academic backgrounds, professional experiences, and skills.

If you’re thinking DS sounds a bit more vague than actuarial science, you’re absolutely right. Data scientists work across many different fields, including big tech, corporate companies, finance, insurance, and more. Actuaries, on the other hand, work almost exclusively within the insurance industry due to their specialisation.

Another key difference is the history of each profession. Actuarial science first emerged in the late 17th century, while data science is a product of the 21st century.

So, what’s the benefit of actuaries doubling as data scientists?

In companies with a need for both, the standard approach is two separate teams. Because of how different the two fields of study are, these teams are structurally very different. As this article by Forbes points out, actuaries have more clearly defined career paths and roles than data scientists, whose roles evolve as companies become increasingly smart and direct about their specific DS needs.

“Many life insurance data science teams started as aspirational. Their expertise wasn't needed as part of the normal value chain of selling life insurance. While they can prove value in improving certain processes, it was harder to measure and justify,” says Ben Tran, Lead Actuary at Montoux. “That’s changed, data science has the ability to change the value chain in life insurance companies. If you’re a life insurer implementing accelerated underwriting, you need a data scientist to maintain this offering.”

Keeping this in mind, here are three key reasons an insurance company might want to train actuaries to be data scientists.

1. Actuaries already have a deep understanding of subject matter, a valuable knowledge base to bring to a data scientist role.

Because actuaries are the experts on insurance data, training them as data scientists combines the depth of knowledge characteristic of an actuary with the breadth of knowledge a data scientist has.

“This approach means actuaries exploring unstructured data and using their knowledge to apply it in a different way. It’s giving them tools to further improve performance,” explains Montoux CEO Geoff Keast. “There’s so much data, but it’s not necessarily being used in an optimal way. There needs to be a more measured approach to the amount of data available.”

After receiving the proper training, actuaries can provide this measured approach by applying their strong industry knowledge to larger quantities of data than a typical actuary would.

2. Difficulty attracting data scientists to a seemingly boring industry - yes, we’re referring to insurance (again).

We’ve touched on this topic previously, but it’s incredibly relevant in this situation. When looking to hire top talent for data scientist positions within the insurance industry, companies compete with top companies in a broad range of industries, including big tech and the business sector.

This high competition means, out of the limited number of highly skilled data scientists, there is a lack of top data scientists looking to begin a career in insurance. This may be because of more attractive options at big tech companies like Google and Amazon, or it may just be because insurance is perceived to be a boring industry. Whatever the reason, there is an acknowledged lack of available data science talent in the insurance industry, something Guardian Life specifically mentioned in its announcement.

3. It adds something to the actuarial role, which helps attract better candidates.

It’s no secret: most new entrants to the job market right now are looking for jobs and companies providing professional development and opportunities for individuals to expand their skills and expertise.

DS is now something taught and tested by the Society of Actuaries (SOA), so more recent actuaries already have a good understanding of what data science is, how it works, and what the role of a data scientist is. This approach gives companies the chance to build off prior education and turn actuaries into full-fledged data scientists.

By training actuaries to become data scientists, you provide an excellent learning opportunity for new recruits joining your company to expand and grow in a new, dual position. Doing so would very likely improve your chances of landing a top candidate since you’re offering a competitive and attractive alternative to a more standard, entry level actuarial role.

4. You’re still working out exactly what is needed from a data science team.

According to Digital Insurance, one of the challenges facing insurance companies is that it “can be tough to tie to specific, compelling return on investment.”

Because the data scientist role in the insurance industry is only recently coming into focus, training an actuary in DS allows companies to explore different DS opportunities through a highly skilled employee with a deep industry knowledge base. This serves as a good opportunity to make the most of this professionally qualified individual in a way that both stimulates their professional growth and expands your company’s approach to consumer data.

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