big data

The race to develop accelerated products has driven life insurers to cautiously embrace the next generation of data.

Could the advances in artificial intelligence that have been applied to the game of chess enhance actuarial science and underwriting? While data drives machine learning today, RGA's Dr. Jeff Heaton notes that Google's AlphaZero demonstrated how a computer can teach itself chess without data.
 

For some, there’s no spookier scenario than blood work and cumbersome forms to obtain insurance coverage. But as Neil Parkin of RGA South Africa explains, emerging risk segmentation techniques, combined with smarter use of data, could help ease these fears. 
 

Now, there’s a movement in the insurance industry to leverage new forms of surveillance to assess risk during the underwriting process of life insurance policies (the process of assessing a potential customer’s risk).

Lest anyone think the previous exchange is fanciful or seems a bit paranoid, it is already happening. It is known as using accelerated underwriting using external data and over two dozen U.S. insurers are currently using it.

More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can have severe consequences affecting individuals as well as groups and whole societies.

The Life Predictive Analytics Survey Report from Willis Towers Watson

RGA's Marc Sofer, Head of Data and Strategic Analytics, Asian Markets, discusses how the newest data sets, together with existing data and novel analytics, are impacting the industry’s growth and development.

Underwriting in the US life insurance industry has had more change in the last five years than it has in the prior 30…and many underwriters are struggling to keep up with the pace. Terms like accelerated underwriting, automated underwriting, simplified issue, predictive models and big data are bounced around at industry meetings like ping pong balls. If you are confused by all the new terminology, you are not alone.