Predictive modeling will change insurance forever! I got psyched about this when, for a current assignment, I re-read my article on workers’ compensation predictive modeling that was published about three weeks ago.
Truthfully, I rarely re-read anything I publish because I get tired of the topic. My goal for the article was write the most comprehensive story on workers’ compensation predictive modeling since most articles on the subject only cover individual aspects of it.
By reading it afresh, I could see all the pieces of predictive modeling coming together to create the ultimate — predictive modeling integration! To explain, here’s a quick definition: Predictive modeling, also known as predictive analytics, uses data and analytical techniques to reveal and create the best predictive risk indicators. Credit scoring in personal auto, while controversial, is a good example.
In workers’ compensation, predictive modeling will transform best practices into employer necessities. Why? Because predictive modeling goes beyond influencing the experience modification factor. It is a more finely-tuned underwriting instrument, enabling insurers to better able to identify and reward the employers who are truly investing in workplace safety, workers’ compensation and return-to-work programs.
I believe integrated predictive modeling will help smooth out the famously cyclical workers’ compensation system. It will change everything from how injured workers are serviced to discussions at the public policy level.
We are not there — yet. I am not aware of any organizations that have connected different predictive modeling applications to maximize its potential. Barriers to implementing predictive modeling abound, but nobody has ever mentioned to me the most obvious: the lack of clear communication between professionals from different specialties!
I mean really, when was the last time you saw an actuary and a claims professional
having a beer and doing some serious integrative strategic thinking?
In fact, because predictive modeling is not being communicated very effectively, everyone does not understand what predictive modeling is and what it can do. Insurance professionals are “siloed” in different departments and disciplines. Predictive modeling is the work of actuaries and statisticians who, as a profession, are not known for being clear communicators.
Self-insured, self-administered companies could reach the ultimate in predictive modeling before insurers if they can get CFO and CEO buy-in. Such a commitment requires communicating a compelling business case, which is also a challenge.
However, insurers are increasingly seeing they must buy-in to compete and improve profit potential. So for this blog, I am focusing only on workers’ compensation insurers and their employer clients. Predictive modeling is most mature in pricing and underwriting. Premium auditing also has maturity because it looks at many of the same factors as underwriting.
Naturally, you would think professionals from the premium auditing and underwriting departments are hanging out at lunch and discussing how premium audit results should affect its future premium. But since these departments have traditionally been separate and apart, the missing dialog is denying this potential to too many insurers.
Claims predictive modeling gets me the most excited because it can detect adverse claim events, prompting attention that can ultimately reduce severity costs and help injured workers get better and back to work faster. The cost containment potential savings for employers is also fantastic! But claims people don’t hang out with underwriters, premium auditors and actuaries.
I mean really, when was the last time you saw an actuary and a claims professional having a beer and doing some serious integrative strategic thinking?
Since each group is struggling through its own predictive models without talking to one another, integrating predictive modeling has a way to go.
But once integration takes place, claims predictive modeling will ultimately provide different and better variables to create finer-tuned predictive models for underwriting and pricing. Then the business argument for employers to improve their workers’ compensation programs becomes even more compelling than the experience mod.
This is just the beginning of synergistic thrill of integrated predictive modeling. The excitement intensifies even more when predictive modeling is applied and integrated with benefits, such as health care, and other commercial insurance lines.
I feel so blessed to see the big picture! More to come in a future blog.