Within the last two years major changes have occurred in the way a typical Arizona term life insurance policy is underwritten. Pre-COVID, life insurance companies, employing a rules-based approach, would start with assumptions of risk and predict an outcome. Today, many insurers are now using a predictive model-based strategy. Predictive models begin with outcomes and use modelling techniques to identify data characteristics that likely result in such outcomes. Serving as a safety net the insurer will also employ rules-based underwriting.
What?
It’s actually not as complicated as it sounds. For many businesses, and especially insurance companies, data is king! As computing capabilities increase, so does the development of predictive models that use machine learning techniques. The end result is a life insurance company that is much more accurate in their mortality assessments. Predictive models also benefit the purchaser of life insurance by removing intrusive requirements (paramedic exam, drawing blood, etc.) and long delays in application underwriting and processing. The combination of predictive modelling and new underwriting data sources has led to instant online term life insurance that does not require a medical exam.
Underwriting Data Sources
Algorithms are used to instantly correlate information from the online term life insurance application, motor vehicle reports, Medical Insurance Bureau (MIB,) prescription history, and in some states’ the criminal history record of the applicant. In the near future clinical lab histories and credit-based mortality scores will also be added to the instant underwriting process of many life insurance companies.
The end result thanks to these advances in life insurance underwriting has been a much more seamless, less intrusive, less expensive, and far quicker process than a short time ago. That an individual can instantly purchase a million dollars or more of term life insurance online within five minutes is truly amazing, again thanks to these recent technological advances.