The Life Predictive Analytics Survey Report from Willis Towers Watson
There was a time in the very early days of life-insurance underwriting, somewhere actually in the 1700s, when the only useful and predictive piece of data recognized was age. And using it affected a simple equation: as the certainty of death approached year by year, so was risk duly assessed. It would take another two hundred years or so before we began to understand that gender also directly impacts the science of mortality. We now utilize a wealth of information to analyze and quantify risk, and yet, it appears, we may have still only scratched the surface. Today, we could be standing at the precipice of a quantum leap in how we understand, interpret and eventually underwrite an ever-expanding inventory of exposures.
Historically, insurance premiums were differentiated only by age, with gender (now removed in some markets) and smoking incorporated later. The introduction of a numerical rating system 100 years ago meant underwriters could immediately better differentiate medical risk. This allowed them to broaden their offers of cover beyond “only healthy individuals”, thereby realising the significant economic potential and much greater inclusion by extending cover to so-called substandard risks.
RGA's Marc Sofer, Head of Data and Strategic Analytics, Asian Markets, discusses how the newest data sets, together with existing data and novel analytics, are impacting the industry’s growth and development.
Facial analytics has the potential to streamline the underwriting process. Some insurers are investigating this new technology.
Insurers seem to be chasing appearances, investing eye-watering sums of money into projects to improve the application process, but these sleek, web-based systems can mask a distressing reality: Underwriting technology underpinning the decision process is not attracting the same attention. RGA's Bruce Bosco calls for a greater emphasis on automated underwriting to improve the customer experience and help bridge the protection gap.
Wearable technology could dramatically change the way life carriers do business and interact with their customers. See what some insurers are already doing with wearables.
So how do insurers unlock value from big data? Jeff Heaton, RGA’s Chief Data Scientist, published author and professor at Washington University in St. Louis, has a few ideas. To start, he suggests it’s time for insurers to better understand the basics of data science. To that end, he self-produced a video to explain the basics in just four minutes. RGA sat down with Heaton to discuss the video and his thoughts on what every employee at an insurance company should know about this form of statistics.
Indirect use of discrimination factors that are outlawed is inexcusable and needs to be avoided by due diligence by insurer and indeed the firm supplying the data. Any decent carrier would not want to cross those red lines anyway – and that is even if the data company involved is not itself subject to regulatory oversight and/or consumer protection laws. Moral: act with integrity and choose your business partners carefully.
There’s not enough oversight for apps that track everything from people’s fitness routines to their menstrual cycles, bioethicists say.