Introduction to Modern Underwriting
Munich Re Life US, in collaboration with Klarity, a UK-based health data analytics firm, is pioneering the integration of third-party longitudinal data and wearable technology into life insurance underwriting to refine and improve risk assessments.
The Shift Towards Accelerated Underwriting (AUW)
The life insurance industry is experiencing a significant shift toward accelerated underwriting (AUW) programmes, driven by the need to streamline processes and reduce reliance on traditional methods. Over the past decade, many insurers have moved away from requirements such as blood tests, urine samples, and paramedical exams. Instead, they now turn to third-party data sources, including prescription history, medical claims, and aggregated lab results, to assess risk more accurately and efficiently.
Role of Klarity in the Transition
Klarity has played a role in this shift, leveraging a wide range of data to help insurers improve their underwriting decisions. As AUW grows in popularity, third-party data is becoming an increasingly valuable tool in life insurance underwriting. Klarity has focused on gathering data from various sources, including motor vehicle records, medical history databases, and wearable technology. These data sources allow insurers to bypass traditional medical tests while maintaining accuracy in their evaluations.
Case Study: Munich Re Life US and Klarity Collaboration
A notable example of this transformation is the collaboration between Munich Re Life US and Klarity, where the UK Biobank—a longitudinal database with health data from over 500,000 individuals—was used to explore mortality and morbidity trends. The insights generated from this project highlight the power of third-party data in refining underwriting practices and making more informed decisions.
The Role of Wearable Technology
Wearable technology is playing an ever-growing role in the evolution of underwriting, with Klarity utilizing data from devices that track physical activity, heart rate, and sleep patterns. These wearables are valuable not only in AUW but also in full underwriting (FUW) and wellness programmes, offering insurers real-time insights into an individual’s lifestyle and health. By analysing data from wearables, Klarity has provided insights that can help predict long-term health risks and improve the precision of risk assessments.
Insights from Klarity’s Research
Klarity’s research shows that individuals who engage in regular physical activity or maintain consistent sleep patterns tend to exhibit better health outcomes. This suggests that incorporating wearable data into underwriting could lead to more comprehensive risk evaluations. Additionally, wearable data has the potential to inform wellness programmes that benefit both insurers and policyholders by encouraging healthier behaviours.
Refining Underwriting Practices with Data
Klarity’s collaboration with Munich Re Life US and the UK Biobank is focused on harnessing the predictive potential of longitudinal data to refine underwriting assumptions. By applying third-party data, including physical measurements, activity levels, and sleep patterns, to a simulated insurance pool, Klarity’s research has provided valuable insights that can be used to improve underwriting models. These findings indicate that health factors such as activity levels and heart rate could have significant impacts on mortality, making them valuable additions to the underwriting process.
Exploring Alternative Health Indicators
Beyond traditional metrics like BMI and cholesterol, Klarity’s research suggests that insurers could benefit from incorporating additional health indicators, such as waist-to-height ratio. By exploring these alternatives, Klarity’s work helps refine underwriting practices and offers a more nuanced approach to assessing individual health. These insights will be shared by Klarity through white papers and research publications, offering guidance for life insurers looking to adopt more accurate and effective underwriting methods.
Challenges and Considerations
While the potential benefits of using third-party data are significant, Klarity acknowledges the challenges of integrating this data into existing underwriting models. For example, wearable data used in their research was collected in controlled environments where participants wore wrist accelerometers and hand dynamometers. Replicating this controlled data collection process on a large scale could prove challenging for insurers. Additionally, Klarity’s research suggests that self-selection bias may be present, as individuals who choose to provide wearable data are likely healthier and more physically active than the general population. This bias must be taken into account when incorporating wearables into risk assessments.
Despite these challenges, Klarity’s research underscores the potential of third-party data in life insurance underwriting. By refining underwriting models with these advanced data sources, Klarity is helping insurers not only improve the efficiency of their processes but also offer more personalised policies to individuals, marking a significant step forward in the life insurance industry.
