Since the first actuarial profession began in 1762, analytics has been an integral part of business decisions in the insurance industry. With the advancements in technology, data availability and development of innovative analytical techniques, analytics has increasingly played a significant role in how insurance businesses function.
There is currently a major evolution going on in the analytics available to and used by insurance companies all over the world. Analytics is moving from a reactive model that was able to support existing business decisions and verify existing actuarial analysis to a new predictive, proactive approach that can improve business by optimizing processes before events happen.
This shift is essentially the next stage in the maturity of analytics – predictive modeling and analytics – according to thought leaders at the Workplace Safety and Insurance Board (WSIB).
“Predictive analytics has become an essential requirement of an insurance company; it is no longer something that’s just ‘nice’ to have,” said Eugene Wen, WSIB Vice President and Chief Statistician. “From the senior executive perspectives, we must support predictive analytics as part of the long-term investment strategy. It is our responsibility to finance predictive analytics and bring its new modeling techniques to the front line so they can improve daily business decisions.”
“Whenever new technology becomes available, it offers opportunities to change the way we do things, and its adoption separates the trailblazers from those that get left behind. The time for predictive analytics is now,” said Wen. In the recent years, most insurance companies have invested in new technology to some degree but, at this time, they are at different stages. Some are in advanced usage mode while others are still in their early stages of adopting and applying analytics.
“It is clear that predictive analytics is the next level up from working on existing measures,” said WSIB Statistician Dragos Daniel Capan. “It is providing early benefits and different insights to each business area where it is applied.”
There are some challenges that remain, however, especially in the realm of timely access.
“Good decision making depends on reliable and timely information. So we need to provide real-time or close to real-time results. The downside of the commonly used descriptive analytics is that the results are reactive, whereas predictive analytics allows us to be proactive. For that, however, we need timely access to information and sometimes that is a challenge.” Capan said. ”The other challenge facing predictive modeling is that many existing databases were not built with predictive analytics in mind. That means risk factors identified in the literature or through internal studies may be hard to locate. This is currently being overcome through new interaction points such as unstructured data analysis and improved data collection", noted Capan
To truly take advantage of predictive modeling, executives must support the framework it needs. While finance is the most obvious piece of this framework, there are three other essential elements that must be in place for proper growth. Wen defines these three as:
With these three elements in place, insurance companies will be ready to move to the next phase of analytics and improve almost all business processes and the customer experience.
Eugene and Dragos are presenting at the Analytics for Insurance Canada (Toronto, May 11-12). Join! http://www.analytics-for-insurance.com/canada/index.php