(Posted by Guest Blogger Dan Dolberger)
Insurance Technology published an extensive feature last week describing how insurers nowadays are seeking a competitive edge through underwriting and pricing analytics.
I read that thanks to predictive analytics, advanced large carriers are today able to automatically process 80-95% of personal lines applications; and that with sophisticated iterations of predictive models the same business benefits can be generated today using less data. I also read how predictive analytics help create super refined pricing methodologies, generating billions of pricing cells, resulting in what some call “perfectly accurate risk pricing”.
I believe another application of predictive analytics should be emphasized – understanding consumer preferences, demand, and lifetime value.
It seems that today’s carriers aren’t lacking in advanced analytics for optimizing the cost of risk. And it is equally evident that getting perfectly accurate pricing on the micro-segment is still not a sufficient condition for growth.
Well, that’s the supply side.
What about using predictive analytics not just to streamline underwriting, not just to price risks, but to manage volume and profit through modeling consumer preferences for insurance products, and their lifetime value to the insurer? The impact such applications have on growth is clear and proven.
February 29, 2008 at 1:55 am |
Great post Dan. Once companies have got some of their core decisions under management the next step is clearly to optimize them and to link supply-side and demand-side decision making. Optimized decisions, where resources are ideally matched to demand will be key to survival as more and more companies develop a decision management competency.
JT
James Taylor
Author, with Neil Raden, of Smart (enough) Systems