While predictive analytics for insurance has become a hot topic in the last year, implementation of analytic tools has been mixed. Some suggest that successful insurers will be those who not only embrace predictive analytics, but embed them in their technology and process. We’d like to know what you think.
What are Predictive Analytics and Why Are They Important?
Wikipeadia describes predictive analytics as encompassing “a variety of techniques from statistics, modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future, or otherwise unknown, events.” The most obvious implementation is Amazon.com which uses knowledge of its customers’ buying habits to offer suggestions of products which might locically follow other choices
We have used this space to cite examples of analytics’ uses by insurers, as well as by agents/brokers. These include the ability to help simplify the on-line quotation process, help identify potential fraud, and target specific offerings based on social media data.
Why Has The Uptake Been Slow?
In spite of real success stories, there has not been a stampede to adopt predictive analytics. Based on recent research on claims, Accenture reports on its blog: “34 percent of insurers currently use predictive modeling, while 32 percent would like to use it.”
The primary reason appears to be the difficulty in aggregating data from the insurers’ existing systems. The Accenture report, North American Claims Investment Survey: A Foot in Today, a Leap into Tomorrow for P&C Claims Functions, notes “insurers with core claims systems more than 5 years old (more than half of the survey sample) saw themselves as much less able to deal with the problems of responding to changes in business processes, addressing consumers’ evolving needs, integrating with other systems and allowing changes in systems behavior and business processes without IT intervention.”
What are the Implications?
There is urgency here, as competitors are realizing benefits from the use of analytics now and are planning for Phase 2 – embedding predictive analytics into processing systems.
In a recent article in Insurance Networking News, Chris Mcmahon documents examples of several insurers who have adopted the use of analytics for claims and underwriting and are now positioned to leverage these investments in further automation. He writes: “These second-generation predictive analytics applications are accessing core systems, data marts and warehouses, external data and synthetic data, and disappearing from view as they become more integrated with core applications, business intelligence and workflow or migrate to the cloud.”
In regards impact of this on others, Mcmahon quotes Brian Stoll, senior consultant and director of Towers Watson’s P&C predictive modeling practice, saying “One of my former bosses once said, ‘Our greatest competitive advantage is that our competitors are other insurance companies.’ They haven’t changed and don’t see any need to change.”
Back in January, we noted that a several insurance experts in the Insurance 2023 Study Group had identified the use of analytics generally, and predictive analytics specifically, would be a differentiator for insurers and brokers alike. One of the brokers in the group noted insurers use of predictive analytics will advantage brokers, allowing them to be more competitive against direct marketers and allow the competition to move beyond price alone, to incorporate service and product features.
What Do You Think?
I f you are an insurer, are predictive analytics part of your current environment or are they on your radar screen? If you are a broker, would you like to see more analytics used by your carriers or are you putting these into your own environment?