Data Analytics and Underwriting: Case Studies in Applied Risk Aggregation
Risk aggregation techniques such as these are useful for a range of underwriting applications; however, a significant degree of subjectivity may still be required in making a decision. Even the most experienced underwriters can find this challenging. In such cases, data analytics offers a potential solution.
I always remember being told, early in my underwriting career (I’ll leave you to guess when that might have been), that it was the duty of the underwriter to accept as many cases as possible at the ordinary (standard) rate of premium – while maintaining equity between policyholders, of course. This was in the interests of inclusivity: making affordable insurance available to as wide a group as possible.
Artificial intelligence (AI) is touching every industry and vertical. Hannover Re’s Senior Data Scientist Julia Perl describes five ways in which AI is reshaping insurance and changing business models.
Have you noticed that the word invasive is being bandied about more and more often in underwriting-related articles and commentaries published online and in various industry publications? This is mainly being done by those advocating radical changes in underwriting practices.
The purpose of this paper is to discuss the insurability implications of low normal/ below normal ALT in the elderly… in the hope that insurers will consider adding appropriate guidelines for this finding at older ages.