The Data Science revolution is not going away and it pays for actuaries to prepare for the impact that big data is having on the insurance industry and their careers. The Healthcare and Property and Casualty industries have led the way in establishing the Data Science/Actuary hybrid.
Josh Wills, Director of Data Engineering at Slack and formerly with Cloudera, Google and Indeed.com, said a Data Scientist is defined as a “Person who is better at statistics than any software engineer and better at software engineering than any statistician.” So is an advanced skill set in information technology the only difference? Why do actuaries pass all those statistical tests if they are going to spend all their time programming?
Yes, actuaries have the core statistical training, analytical thinking ,quantitative skills and advanced domain knowledge needed to succeed as actuaries in the insurance industry. But do they have the skills to adapt to the impact of dynamic risk management? Rob Thomas, VP of IBM Analytics, defines this term: “Dynamic risk management is an accelerated form of actuarial science. Recall that actuarial science is about collecting all pertinent data, using models and expertise to factor risk, and then making a decision. Dynamic risk management entails real-time decision making based on a stream of data.”
What does a successful actuary need to do dynamic risk management?
To work with big data, actuaries need the ability to subdue unstructured data, better programming skills, visual representation experience and the skill to build models in a big data world. The Working Party of the Casualty Actuarial Society summarizes these skills brilliantly in the overlapping circles shown at left. Actuaries have great substantive expertise and statistical knowledge putting them as leaders in traditional research. But to be true data scientists they must have the technology skills to interact with both their statistical and domain knowledge.
How do actuaries get data science skills?
Actuaries must get hands-on experience with large data sets. These data sets are in the millions and billions of data points. Certainly using SAS and R for the analysis is required, but expertise in corralling all the available unstructured data with Hadoop, Python or Spark is becoming essential. Many actuaries feel progressing in their careers means moving away from hands-on work to managing hands-on staff. Not true in the data science revolution. Senior level managers making $200K+ still handle data and still lead the charge with the rapidly changing technology applications.
Need more information and help making this transition? Contact Rory Hauser, Practice Lead for Actuarial Recruiting at Executive Recruiters Smith Hanley Associates at 203.319-4305 or email@example.com.