Shifting the Insurance industry from a reactive to proactive business model

The current way insurance carriers leverage data is antiquated and is usually a knee jerk reaction based on “descriptive analytics” that provides the executive team with a post mortem view of sales vs. goals or profitable plans sold vs. unprofitable ones. While some executives may view this as an actionable roadmap towards adjusting pricing and offerings for the following year, it is in fact a very limited and probably outdated view of their entire customer base.

By incorporating technology that leverages “predictive analytics”, carriers will have both a telescopic and microscopic view of their members. Think of Predictive analytics as a way to sit with every member or prospect, communicating in real-time what they like, why they left a page, or why a specific plan or product resonated with them. By adopting to a “what is going to happen” way of thinking, carriers will be able to apply predictive analytics to dive into the challenges of population health management tasks, adjust marketing and sales plans based on demographics or audience segments and quickly react to meet sales goals and revenue opportunities, all while improving the overall customer experience.


The insurance industry works on the principle of risk. Customers take out policies based on their assessment of a particularly bad thing happening to them, and insurers offer them cover based on their assessment of the cost of covering any claims.

So wouldn’t it benefit everyone if there was a way to more accurately assess risks? Well, it turns out that in age of Big Data there is. Big Data as many will be aware by now is a buzzword which refers to the ever increasing amount of digital information being generated and stored, and the advanced analytics procedures which are being developed to help make sense of this data. Predictive, statistical modelling basically means working out what will happen in the future by measuring and understanding as much as we possibly can about what has happened in the past. “Models” are then built which show what is likely to happen in the future, based on the relationships between variables which we know to exit from examining the collected data from the past. It is a key tool in the Big Data scientist’s toolkit, and insurance (predictably) has been one industry that has been very keen to adopt it.

So in this article Bernard Marr will take an look at some of the more recent developments in the insurance industry, which have become available thanks to our increasing ability to record, store and analyze data.

One of the most important uses is for setting policy premiums. In insurance, efficiency is an important keyword. Insurers must set the price of premiums at a level which ensures them a profit by covering their risk, but also fits with the budget of the customer – otherwise they will go elsewhere. Read the entire story here


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