Good news...an analytics survey last year found that 72% of insurance executive agreed that analytics is the biggest game-changer in the next 2 years. Bad news...compared to other industries the adoption rates of analytics in the insurance has lagged other industries. To reverse this trend and help insurers travel down the
The insurance industry is heading for a crisis. Depending on which report you read the insurance industry is facing a shortfall in job vacancy from anything from 40,000 to nearly half million in the next few years. Baby boomers in specialized jobs like underwriters and claims adjusters are retiring and insurers
If you buying or selling a house. The relator will tell the value of the property is all about location, location, location. For insurance companies location is just as important. For an underwriter assessing the risk on a property is essential that they consider the location of the property. How
Who is your best customer? The answer to this question can vary dramatically depending on your industry. A retailer’s best customer is someone who comes back to their store over and over again. A gym owner’s best customer could be considered consumer who pays their monthly on time but never
The analytical lifecycle is iterative and interactive in nature. The process is not a one and done exercise, insurance companies need to continuously evaluate and manage its growing model portfolio. In the last of four articles on the analytical lifecycle, this blog will cover the model management process. Model management
Insurance relies on the ability to predict future claims or loss exposure based on historical information and experience. However, insurers face an uncertain future due to spiraling operational costs, escalating regulatory pressures, increasing competition and greater customer expectations. More than ever, insurance companies need to optimize their business processes. But
There is no doubt that analytics is an overused and often abused term. So what does really analytics means? In part 2 of a series of articles on the analytical lifecycle, this blog will highlight some of the common and emerging techniques used to analyze data and build predictive models
In my last blog I detailed the four primary steps within the analytical lifecycle. The first and most time consuming step is data preparation. Many consider the term “Big Data” overhyped, and certainly overused. But there is no doubt that the explosion of new data is turning the insurance business
Advances in technology, evolution of the distribution channels, demographic shift, economic conditions and regulations changes. How does an insurer prioritize all these seemingly competing goals and create sustainable competitive advantage. One answer is analytics. Many insurance companies are just beginning to take steps toward becoming an “analytic insurer” – one
The old adage is that “Data is the lifeblood of the insurance industry.” However, for many insurance companies, data is like the red-headed stepchild. No one is willing to take care or have responsibility for it. In the past, insurance companies have created data governance programs, but these have often
