Over the past few decades the amount of data to help us better understand an individual’s health and predict mortality has increased immensely, most notably over the past 10 to 15 years.
The continued growth for insurtech companies also continues to present new data and methodologies to improve the insurance industry.
Unfortunately, activity level information continues to be overlooked as an important measure to assess one’s health and expected longevity, or has only been measured 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 for stratifying and determining risk class criterion and placement for applicants.
While each is an important health metric, these traditional approaches and metrics can often ignore important indicators of an applicant’s health profile because the measures only provide part of the health history and often miss important individualised measures such as resting heart rate, heart recovery rate, sleep and activity versus inactivity levels.
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.
Current state of the industry
The life insurance industry has incorporated and used new data sources but at a slower rate than other industries and other types of insurance.
In addition to use of external data sources such as motor vehicle records, some of the largest movements in adding data for risk selection include credit risk attribute scores, pharmacy data records, medical billing data, clinical laboratory results data and electronic health record data — most of which are heavily leveraged for some variation of accelerated or automated underwriting.
Companies have had vastly different experiences with accelerated and automated underwriting programmes. Some have observed only slight differences (‘mortality slippage’) between the mortality for policies underwritten using traditional criteria and data elements compared against policies incorporating more data and model driven criteria. Meanwhile, others have observed significant mortality slippage.
Additionally, the process for accurate risk classification/stratification has been quite challenging for many companies, especially between the preferred risk classes that historically were differentiated by factors obtained through more invasive, time-consuming and expensive bodily fluid collection and measurements such as height, weight and blood pressure.
More recently, a positive trend has emerged: the explosion of wearable technology has resulted in individuals increasing their awareness of and focus on being well informed of their individual health and wellness.
Access to wearable data is now widely available via smart devices, including watches, bands, scales, rings, cell phones, computer applications and clothing.
In the UK, growth in fitness tracking has been steady. Many adults now own a smartwatch and a number of these use dedicated fitness trackers, while smartphone ownership stands at the vast proportion of the population.
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