Augmented and automated underwriting using machine learning

Abstract

A generation of internet shoppers who are accustomed to researching, purchasing, and evaluating items has joined the insurance industry, yet they are met with a purchasing experience that falls short of their expectations as "digital natives". As a result, the industry is always looking for new ways to fully underwrite individual applicants in less intrusive, cost- and time-effective ways. In order to enhance customer experience, technology has rightly been seen as a major part of the so-lution to the challenges facing the insurance industry. Risk prediction, which is central to enhanc-ing efficiency, alleviating cost pressures, improving insights and reporting and delivering a supe-rior customer experience, has been a major part which insurance companies have transformed by introducing Machine learning and AI models to solve the challenges that have been faced for over decades ago. (Mike Batty, 2009) While several papers about automation of insurance have been published, extant research has not quite caught up on the use of AI in the automation and augmentation of insurance. This paper therefore will examine AI’s position in automation of in-surance underwriting and how that role may affect the future of life underwriting