The world is constantly changing, and as an actuary, I probably view these changesdifferently than most people. In my world, all of the advancementsin new and innovative technology that have made our lives moreconvenient also present more complex risks.

From an insurance perspective, new and more prevalenttechnologies like mobile payments and drones require more complexrisk-management tools. Previous methods for quantifying andmanaging risk — such as using past data to price insuranceproducts — may no longer be sufficient. At the same time,the digital revolution, led by smartphones and wearable devices, isgiving us more data than ever before. Insurers need to embrace andmine the increasing volume of data, finding new techniques toevaluate and produce insights.

The good news is that a lot of new data is readily available— the not-so-good news is that insurers and theiranalytical teams may not know what to do with this data. Everythingfrom the sheer volume of data to the nature of how it is stored andprocessed can make it hard to sift through and find informationthat will be useful.

Due to this influx of data, the industry has seen the partneringof actuarial work with data science to perform predictive modeling.Despite the arrival of new techniques, however, insurance remains ahighly specialized and highly regulated industry — thosehandling the data in insurance companies need to fully understandthe business context in which it lives. One variable of data mayrepresent something that is not legal or socially acceptable toactually use in practice, or, data may say something that makes nosense at all – for example, that women with red hair have more auto accidents (when anyone can dye theirhair). So those working on data analytics teams need to have astrong sense of causality when evaluating data, knowing how itplays into the larger business context of the problem they'retrying to solve.

Bridging the communications gap

There is no prescribed composition for an effective dataanalytics team — it can have a mix of data scientists,actuaries, statisticians, and others. Each professional bringssomething to the discussion, and increasingly the "team" approachto analytics results in success. However, those same professionalsneed to understand each other's perspectives — they needto be able to speak the same language in order to communicate andcollaborate. Ideally, members of the team will have a certified setof predictive analytics skills, which can help set a standard andbridge the communication gap that exists.

For employers, this lack of common "language" in the predictiveanalytics environment can also affect their recruitment. Positiontitles such as "data scientist" or "modeler" do not have aconsistent description or industry standard. Last year, when theCasualty ActuarialSociety (CAS) conducted market research with insurance companyexecutives on the subject, employers cited recruiting/hiring as oneof their greatest challenges in predictive analytics. In fact, 76percent of those surveyed noted that a certification would bebeneficial to employers seeking to hire specialists in predictivemodeling.

Becoming a certified predictive analyticsspecialist 

This is one of the many reasons that The CASInstitute, a subsidiary of the CAS, recently launched itsCertified Specialist in Predictive Analytics (CSPA) credential. Thecredential, created for data professionals with several to manyyears' experience, requires that candidates demonstrate evidence ofapplied knowledge in predictive analytics by passing aseries of four assessments. The program draws from the historyand strength of the CAS, whose high-quality educationalstandards and credentialing programs for actuaries have beenrecognized globally for over 100 years.

The curriculum of the CSPA credential is overseen by an expertpanel comprising industry specialists working in predictiveanalytics. The four required assessments cover:

• The fundamentals of property and casualty insurance;
• How data works, including the forms it can take;
• How to present and work with data, including building models;and
• How to apply these skills to a real-life scenario.

The final assessment also asks candidates to complete dataanalysis and a report based on an assigned scenario. The candidateis required to integrate and apply all knowledge from the previousthree assessments in order to achieve success.

Final projects will vary so as to reflect real-world-typepredictive analytics scenarios. For example, one project might havecandidates working to improve claims department operations, such asidentifying potential high-severity claims, or controlling claimsdepartment costs. A marketing-focused project could ask candidatesto improve sales through methods such as matching product offeringsto customer type, or targeting new or optimal customer segments.CSPA candidates may also use their predictive analytics skills inscenarios involving underwriting, pricing, or even operations. This"case study" project helps round out the CSPA curriculum by testingthe candidates' ability to use their predictive analytics skills inthe workplace.

Related: Insurance industry education is more than justletters after your name

A new professional community

CSPA credential holders are also required to complete an ethics course and adhere to a standard ofprofessionalism and code of conduct, something not previouslyrequired of those in analytics roles.

After traveling all over the U.S. sharing information about ournew CSPA credential with employers, we can say that the responsehas been overwhelmingly positive. Employers are enthusiastic to seea program that can provide professional education and certificationto members of their team who have previously been without thesetypes of dedicated resources. Employers now have a reference pointwhen they decide to add predictive analytics professionals to theirstaff. The CAS Institute also provides its members with aprofessional community, where those working in this specializedfield can connect.

Ultimately the expansion of predictive analytics within theinsurance industry has opened doors for new opportunities toimprove business performance. In order to maintain momentum andkeep up with changes, predictive analytics teams need to make surethey are well-equipped and collaborating effectively to adapt tonew technologies and new data. It's only through the improvementand standardization of analytical skills, coupled with thewillingness to learn, that we will remain ready to respond to thetechnological (and societal) changes that still await us.

Nancy Braithwaite, FCAS, MAAA, CPCU, is a Second VicePresident and Actuary in the Excess Casualty Department atTravelers Insurance Co. She currently serves as president of theCasualty Actuarial Society (CAS). Opinions arethe author's own.She can be reached at [email protected]. The CAScan be reached at (703) 276-3100.

Related: Embrace the shift! Transforming the insuranceindustry from the outside-in