Peter Knowles of specialist recruitment agency, IGA Talent Solutions*, on the demand for a new breed of actuary in insurance
I’ve spent the last 15 years predominantly working to recruit actuaries, mostly for the insurance sector. I certainly see continuing demand for traditional actuaries right now. But somewhere around 10 to 20% of the roles that come via the agency are now looking like something a bit different and seem to suggest that the pace of change is increasing.
If actuaries act now, they can do a lot to turn this into an opportunity for the profession.
The reality is that actuaries are seen as an expensive resource by employers. The cost of educating an actuary and the cost of employing many, highly-qualified and regulated professionals weighs on employers heavily.
The adoption of technology offers real efficiencies and this is now moving really quickly within the insurance sector. New applications are coming out to streamline working processes and to deliver additional value over and above what has previously been possible.
The questions are becoming unavoidable: how many human operators do we now need and what sort of skills should they be equipped with?
While I don’t currently see a reduction in the number of actuaries happening, what I am seeing is growth in the number of roles with titles such as Actuarial Technologist or Transformation Actuary.
So it's important to think about what this means for the role of the actuary now and in the next few years.
It’ll come as no surprise to any actuary when I say that what insurance companies want is as much information about their customers as possible, so that it can effectively market its products and reduce its exposure to risk. And this is where data science is delivering real commercial value.
The old example is the supermarket loyalty card. These cards were one of the key reasons why Tesco and Sainsbury’s went into insurance, because they were able to collect lots of different data about your habits and better understand the risks around you as an individual.
Clearly, the better placed actuaries are to exploit such unorthodox sources of data and incorporate that into their work, the stronger their models will be and the higher their value to employers.
So that driver is to some extent about developing the actuary’s technical skills, but the other aspect of the change we’re seeing is in the need for managerial skills.
That might sometimes mean the actuary overseeing data science specialists. But I’m seeing more along the lines of programme manager roles, finding and rolling out new solutions to make a company more efficient across the finance function and more broadly.
In order to implement a new software solution, you need people to understand it, and be on board with it and adopt it. To do that across big, complex organisations takes time, money and effort. The specialist expertise of the actuary means that there is now growing demand for actuarial change managers to make this happen.
Positioning the profession to take advantage of these changes starts with the core curriculum. The content should be considered, of course – embedding coding, systems thinking and managerial skills alongside the mathematical methodologies. But also the time and cost of qualification.
I’m aware that this is something actuaries are very protective of, because their value has always lain in their intellectual and ethical rigour. And while that remains the case, it is a fact that the cost of an actuary is something employers think about carefully.
But it’s also good to see that the IFoA is taking a thorough and demand-led look at how it can support the profession with post-qualification learning and certification. That stuff is good for people's employability and boosting their value add.
Particularly if such accreditations are designed directly on the needs of and perhaps even in partnership with employers, they could be a key part of maintaining the actuarial profession’s high value in insurance for many years to come.
*IGA Talent Solutions participated in the IFoA’s recent research on data science learning.
