Stuart Rose is the Global Insurance Marketing Manager for SAS. He began his career as an actuary and now has more than 25 years of experience in the insurance industry working for companies in the US, Europe and South Africa. Stuart has written many insurance-related articles and is also the co-author of Executive’s Guide to Solvency II.
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
