According to IBM, 90% of all the data in the world has been created in the past two years. Each of us is utilizing and providing data every time we log in to our computers, browse the Internet or simply drive down the highway with our iPhone in our pocket. The challenge for every industry is how to harness and effectively utilize that data to drive more profitable business.
The life insurance industry has relied mostly on the knock-out approach to underwriting preferred risks. When reviewing the thresholds for individual risk criteria, one may ask whether the system is too liberal – i.e., are we admitting too many questionable risks into our best classes? Yet industry experience seems to indicate that the system works well.
In underwriting, the ability to obtain accurate health information from an applicant is paramount. The practice of asking people to report their health has been perfected over decades, yet under-reporting remains an issue in markets around the world. This leads us to consider: is it something about the way we ask the question?
With decades of underwriting experience to research, the author reviews some of the criteria commonly used to try to answer the question: “We have done well in the past, but could the industry use its criteria to make risk selection more effective?”
In my last blog discussing Gen Re’s work on decision biases, I shared how something called the fundamental attribution error inclines us to overweight the contribution that individual attitude and skill makes to our performance and underweight the role of the context in which we work.