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 what does that mean in practice?
In the third part of a series of articles on the analytical lifecycle, this blog focus on an analytics ecosystem for decision making.
Traditionally analytics has been seen as a back-office function. Implemented in silo by different departments and lines of business. Today it is becoming essential that insurance companies embed analytics into real-time business decisions by deploying predictive models into transactional systems like policy administration, claims management or even call centers.
Whether it be underwriters, claims adjusters, risk managers, insurance personal make hundreds and thousands of operational decisions each day that impact an insurance company. For insurance companies that rely on analytical models in the decision process, SAS Decision Manager provides a single point of control to integrate sophisticated analytical models with their business rules. The solution takes data, business rules and analytical models and turns them into consistent, automated actions that drive faster, better operational decisions. In addition, these business decisions are now governed, traceable and fully documented which is essential in the heavily regulated insurance industry.
One organization that recognized that to stay ahead of the competition, it had to manage its information as a strategic assets was Australian insurance company, IAG. They use SAS High Performance Data Mining to analyze its growing database. By using analytical models that used to take hours can be reduced to minutes. Read more about the IAG case study.
Today, insurance companies are becoming data intoxicated as they consume more and more data. But the true value of big data lies not just in having it, but in being able to use it for fast, fact-based decisions that lead to real business value.