Wearable-driven mortality modeling improves underwriting accuracy
“Sitting is the new smoking” is a catchphrase often used to encourage people to get some level of physical activity. Medical personnel, underwriters, actuaries and mortality researchers understand activity level is an important measure to assess one’s health and expected longevity. Unfortunately, activity level information is often overlooked or has been measured only through self-reporting or correlation to other measures such as body mass index (BMI) in the life insurance risk selection process.
Since the proliferation of multiple risk classes, companies have used traditional measures such as cholesterol level, blood pressure, BMI, tobacco usage, and personal and family history, to name a few, for stratifying and determining risk class criterion and placement for applicants. While each is an important health metric, these traditional approaches and metrics often misclass applicants because the measures only provide part of an individual’s health profile and often miss important individualized measures such as resting heart rate, heart recovery rate, sleep and activity versus inactivity levels.
Over the past 10-plus years, the industry has been moving toward changing the underwriting process. For life insurance, this has meant rethinking the data sources used, improving the customer’s experience and shortening the time from application to policy issue. This has added to the challenges for risk classification and difficulty in truly differentiating the risk profiles of the preferred risks as well as the healthier impaired.
The need to rethink the risk stratification process in the life insurance industry has become increasingly evident over the past decade. With the proliferation of new data sources and advancements in technology, there is a significant opportunity to enhance the accuracy and efficiency of underwriting processes. The Klarity model aims to address this need by leveraging nontraditional data to produce individual-level mortality scores that can predict and classify risks more effectively than traditional methods.
Over the past year, WTW’s Insurance Consulting & Technology team has analyzed a new risk scoring tool developed by Klarity, which incorporates data obtained from a wearable device such as a fitness watch, a smartphone or other device that captures activity levels, sleep patterns, heart rate and pulse data.
Key observations of WTW’s analysis show:
The Klarity model demonstrates a promising approach to improving risk stratification in the life insurance industry. One of our key findings is that while the Klarity model validates the risk ranking of traditional underwriting classes used to assess mortality risk by insurers, the Klarity model can improve and further differentiate risks significantly even within a single risk class. We found the risk assignments by the Klarity model to be highly correlated with actual mortality results relative to mortality demographic baselines. By utilizing a broader range of data sources and advanced predictive techniques, the model can provide more accurate and individualized risk assessments, ultimately leading to better pricing, improved customer engagement and enhanced in-force management.
The white paper outlines WTW’s analysis and findings; it’s structured as follows:
