technology

The author makes the case that the new underwriting strategies will continue after the COVID-19 pandemic is over.

Post-human underwriting? In On the Risk, RGA's Dr. Dihui Lai discusses the current state of artificial intelligence and the potential for machine learning, optical character recognition and deep neural networks to augment, but not replace, traditional life underwriting tasks.

Mobile apps are now widely accepted as a self-management tool for managing epilepsy – with smartphone applications used in tandem with traditional treatment and anti-seizure medications. This neurological disorder is a common condition seen by insurers, and many applicants with controlled seizure history can be considered favorably for life insurance.

The center questions the use of factors such as employment class and education level in marketing, underwriting and pricing.

Predictive models are an incredibly powerful tool with the potential to drive the life insurance industry forward in ways that are good for both consumers (improving their purchasing experience by removing intrusive requirements and long delays) and carriers (increasing taken rates and persistency, and increasing the accuracy of mortality assessments).

Life insurers need actionable responses to current underwriting challenges – including the need for fluidless Accelerated Underwriting alternatives, while striving to maintain the same level of mortality.
 

This two-part article aims to review some of the important concepts and considerations for insurers wishing to use data and technology to become a partner rather than just a payer for their policyholders. These include ethical considerations, understanding exactly what data is, the implications of data misuse and of ethical intelligent data use, and the importance of integrating data from various sources, ultimately enabling us to grasp the true potential of data.

This two-part article aims to review some of the important concepts and considerations for insurers wishing to use data and technology to become a partner rather than just a payer for their policyholders. These include ethical considerations, understanding exactly what data is, the implications of data misuse and of ethical intelligent data use, and the importance of integrating data from various sources, ultimately enabling us to grasp the true potential of data.

Medical records are not only a crucial piece of the underwriting process but also one of the costliest in terms of time spent – positioned for transformation through data science and machine learning.

The importance of AI to the insurance world has grown in tandem with insurers' reliance on data. Hear how AI is impacting the industry.