By introducing this we are aware that data era has begun to spark a wave of innovation in the insurance sector. Big data has the potential to create sophisticated risks models that are focused on individuals, extremely accurate and capable of being updated in real time..
As actuaries we have a major challenge in that, the data scientists are already competing with us in some of our employment fields. This may be a future uncertainty to our employment fields. Therefore, we have to lay down strategies to deal with this uncertainty. Data scientists have focused in particular domains of mathematics, statistics ,information science and computer science which include machine learning, classification, cluster analysis, data mining databases and visualization. We should not be afraid but take up the challenge and see how we can solve it. Some of the reasons that have led to data scientists skyrocketing over us are; they do not take a bunch of exams as we do, they have an overlap in areas of data analysis and we concentrate on reserving only while data scientists take on more advanced computer programming and data engineering. Salary wise the data scientist’s salary is already skyrocketing while that of actuaries is stabilizing. May be this is the reasons as to why actuaries are running to data science side.
Although we both focus on statistical modelling concepts such as linear regression and time series data, they have the advantage in that they can find employment in a much broader array for industries. As actuaries this should not intimidate us, we have to work to our level best so as to prove them wrong. Every actuarial personnel should know, ‘we can not run out of work until the world runs out of problems.’ What is the solution to this clash as actuaries? Let us not only concentrate or spend our time in managing and cleaning data but also let us go an extra mile. As we pursue the actuarial path, we should also study advanced programming, data engineering, use of statistical programming languages, understand statistical algorithms and know when and how to use them in the context of problem solving. When we implement and learn these, we will reduce the rate of employment for the data scientists in actuarial fields. Moreover, data scientist may not emphasize on financial economic aspects such as reserving, interests rate and pricing options. Actuaries lets think big and work to break boundaries.