With the rise of big data and machine learning, some would ask where actuaries are positioning themselves in the next few decades. We came up with the term “actuarial scientist” to mean an actuary who possesses the actuarial skills and domain knowledge, coupled with the understanding of how to use today’s technology. You may have heard other terms floating around such as “digital actuary”. In my view there is definitely space for both actuaries and data scientists to thrive in the insurance world for years to come. |
By Cherry Chan FIA, Partner at Barnett Waddingham However, I do believe that there is a necessity in some areas for actuaries to fill more of a hybrid role as an actuary and a data scientist; that is, to be “actuarial scientists.” At Barnett Waddingham, we have been upskilling our team for a few years and converting processes to R, SQL and Python, where appropriate. We still have some way to go, but we’re keen to share with you our vision and some of the things we’ve learned when helping clients.
Our process automation vision Although we haven’t got to the no-touch state yet, we do have some processes that are run with a click of a button to obtain the first cut results. By optimising the time spent on processes, actuaries can spend time on value-added work and have more time to get involved in new areas across the organisation. In this blog, we have used process transformation with R as an example of upskilling our actuaries in using new tools and exploring other ways to build models. These include building bespoke parts of internal capital models, for validation, reserving, pricing and data processing. However, the same will apply if you are upskilling actuaries with other software and tools. Some of our R case studies are on our website and can be found below.
Upskilling our actuarial team – key lessons learned
Find the right expert to train your actuaries
Be patient Some of our earlier projects have taken a few attempts to get them right. It’s important to factor in time for the team to explore and fail. Failure is a learning experience, as even the best team will not get everything right the first time. Fail fast, learn quickly is the lesson! For the actuaries who are learning how to code and are building new tools, it may be frustrating having to look up how to debug unfamiliar error codes in the R models. Or there may be no other team members who know the answers. Muscle memory plays a large part in coding and, with time and practice, you will become more proficient with R or other platforms you may choose.
Keep practising
Cultivate a growth mindset Many actuaries are curious to learn, so we instil a mindset that any skill can be learned as long as you put your mind to it. You probably couldn’t build a cashflow model in Excel straight out of school, but you learned. You might not be able to automate some data checking in R now, but if you put the time in you will learn. Upskilling takes time, effort and a willingness to continually learn.
Horses for courses
Governance
Check outputs match expectations Ensure good version control. One of the advantages of R over Excel is that R can be used with Git and has very good version control capabilities which, in our opinion, are far superior to Microsoft Office.
IT environment Having a good and flexible IT team behind you makes transitioning to R much easier.
Senior managers do not need to be R coders Actually, it encourages and forces us to do more on job training as we review the work done together. I am sharing more of my thought process with my team and, at the same time, I am improving their knowledge.
Be purposeful with your programme of change Upskill your team with an expert who has the technology know-how, the skills to provide bespoke training for your business and the ability to set up best practices for your team to follow Be patient and give space for the team to grow Consider the IT infrastructure requirements and have an IT environment that optimises the tool and platform Be more than just an actuary |
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