Automotive telematics and usage-based insurance. The Internet of Things and Big Data. The cognitive era has indeed arrived in the general insurance (property-casualty) industry. The challenge for insurance companies is figuring out how to intelligently leverage all the fresh data, not only to effectively manage the escalating and ever-morphing risks posed to personal and intellectual property, but also to stay a step ahead in what is a highly competitive, global general insurance market.
The key to meeting that challenge is predictive analytics — and an actuarial force that knows how and when to use them, according to Anthony Cappelletti, FSA, FCAS, FCIA, general insurance staff fellow at the Society of Actuaries (SOA).
“Claims settlement, ratemaking, fraud detection, loss reserving, operations, underwriting — there are so many different ways insurers can use predictive modeling and Big Data to be more profitable and competitive,” he says. “Actuaries who understand predictive analytics — how to design systems, develop them and interpret their output — are the ones who are going to add value to the insurers that employ them.”
The best-positioned companies will be those who find ways to integrate traditional approaches with advanced actuarial modeling and analytics tools, and technical skills with a strong global business sense, according to Cappelletti. The consulting firm Deloitte summed up the dynamic in a 2016 report on analytics trends: “As cognitive technology evolves, it is likely to become just another tool in the toolbox — very useful for the right application but not replacing traditional analytics capabilities that also complement the human thought process. The man-machine dichotomy is not ‘either-or.’ It is unequivocally ‘both-and.’”
Now, work is underway to develop a new generation of actuaries who come real-world-ready with a “both-and” skillset: the aptitude to apply both advanced predictive analytics and traditional modeling approaches. In 2016, the SOA Board of Directors approved a plan to revamp its actuarial education for the Associate of the Society of Actuaries (ASA) and Chartered Enterprise Risk Analyst (CERA) curriculum. Set to roll out in July 2018, the updated curriculum, which draws on input from a wide range of industry stakeholders, focuses on strengthening predictive analytics education and training. The goal: develop actuaries whose advanced analytics skills make them an immediate asset to the insurers that employ them.
As part of the curriculum update, the SOA is adding two new elements to the ASA curriculum. The first new element is an ASA exam on statistics for risk modeling. This exam includes an introduction to the generalized linear model (GLM) to give candidates an opportunity to work with the GLM, advanced cluster analysis and other new analytics tools. Case studies can be used to deliver hands-on experience with various modeling techniques and processes. That work is followed with the second new element, a new requirement dedicated exclusively to predictive analytics which will include an assessment of a proctored project completed by the candidate on predictive analytics. In this project, candidates will use computer packages to analyze data sets and communicate their findings. They must show they can use these tools to solve the complex, multifaceted problems they’ll face in the workplace.
The ASA curriculum update replaces the Applied Statistics Validation by Educational Experience (VEE) subject with a Mathematical Statistics VEE subject. This follows from the introduction of the two new elements that include applied statistics but require a foundation in mathematical statistics. The ASA curriculum update has also been used as an opportunity to create a better balance between long-term and short-term actuarial mathematics by increasing coverage of short-term actuarial mathematics. ASA candidates will gain a thorough understanding of the pricing and reserving dynamics associated with short-term products such as general insurance.
SOA’s General Insurance Fellowship curriculum, Cappelletti explains, is built around hands-on e-learning, case study-based application of models to real-world data, and individual project work where candidates package their analysis and findings in a business-style report. The ASA curriculum update, with its heightened focus on predictive analytics, has caused much of the material in the current General Insurance Track Fellowship requirement Applications of Statistical Techniques (AST) module to shift to the ASA curriculum. This change will leave room in the revamped AST module for more in-depth and advanced hands-on experience applying advanced modeling techniques to tackle more complicated scenarios. In addition to this, the balancing of long-term and short-term actuarial mathematics in the ASA curriculum update will permit the General Insurance Fellowship exams to reduce introductory material that will now be included in the ASA curriculum and instead include more advanced material.
In an increasingly uncertain world where existing risks morph and new threats emerge greater than the last, what is certain is that actuarial departments need more than just historical claims data and the basic regression and time series methods to maintain an edge for their shareholders and their customers, says Cappelletti. “Insurance companies need to make use of the technology and data that’s available to them to stay competitive. If they don’t, they’ll be more susceptible to adverse selection.”
The key to harnessing these cognitive resources lies in finding actuaries with the skills to:
- Mine, model and analyze complex data to identify emerging liabilities earlier, before they escalate, and to anticipate areas where new threats are likely to emerge.
- Develop actuarial strategies around such new products as usage-based insurance.
- Gain valuable strategic insight into coverages and products customers need most (or perhaps didn’t even realized they need).
- Improve responsiveness to large-scale events and fast-changing market conditions.
Given the speed at which technology is advancing, and the insidious, rapidly evolving nature of threats such as cybercrime, climate change and disease pandemics, it’s also vital that training programs prepare their general insurance actuaries to make an impact from Day One. The shorter the on-the-job learning curve, the better, observes Cappelletti. “Insurers really want incoming actuaries to bring strong analytics knowledge with them to the job.”
Visit www.soa.org/curriculumchanges for more details.