Traditionally, life insurance underwriting has relied heavily on medical data to assess risk, but the use of data is encouraging insurers to expand their approach by incorporating non-medical data sources, such as driving reports, credit histories, and public records. When used together, these data points create a fuller picture of the applicant, enabling more nuanced risk segmentation, offering new opportunities for both insurers and applicants.
Medical and non-medical data are commonly used in life insurance underwriting workflows, but their simultaneous consideration is less typical. The 2024 Life Insurance Mortality Risk Management Study, published by LexisNexis Risk Solutions, highlights that, by combining medical and non-medical data, insurers can better segment applicants, even within populations traditionally deemed high-risk.
The Shift Toward Data Integration
This trend toward diverse data usage within the underwriting process is a fundamental shift in how risk is assessed. For decades, life insurers used medical data alone to determine an applicant’s insurability. While effective, these methods are often time-consuming and costly. By introducing non-medical data into the equation, insurers can develop a more comprehensive understanding of an individual’s risk profile without relying solely on medical information.
For instance, combining medical data with non-medical insights uncovered that applicants with type 2 diabetes or asthma, typically considered high-risk, might still qualify for accelerated underwriting. Specifically, the research showed that 10% of applicants with type 2 diabetes and 60% with asthma have average or better-than-average mortality risk. However, an applicant with both a recent DUI record and a diagnosis of alcohol abuse was found to have a mortality risk five and a half times higher than average. This level of insight is invaluable for tailoring risk management strategies.
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Enhancing Operational Efficiency
The integration of medical and non-medical data is also transforming the operational efficiency of the underwriting process. The traditional underwriting model often involves multiple layers of review, including medical exams, lab tests, blood draws, and detailed health histories. This process can be slow and drawn out, leading to delays in policy issuance and, in some cases, applicant drop-off.
The study by LexisNexis Risk Solutions found that incorporating non-medical data could reduce the need for invasive medical exams and allow for quicker decision-making. Applicants who might have been flagged for manual review based on medical data alone could be reclassified for accelerated underwriting when their non-medical data suggests lower-than-expected risk. LexisNexis Risk Solutions was able to identify 60% of applicants with asthma who are good candidates for accelerated underwriting, as well as the 20% who warrant closer scrutiny in manual underwriting. With insights like this, carriers can more effectively analyze applications and make optimal use of underwriting resources. This not only speeds up the underwriting process but also reduces costs for insurers, making the entire operation more efficient.
Further, the ability to make faster, data-driven decisions is increasingly important in today’s market. Consumers are becoming more accustomed to seamless, digital-first experiences, and the life insurance industry is no exception. In addition to incorporating non-medical data, the availability of electronic health records (EHRs) can further expedite the life underwriting process. By having near-real-time access to both non-medical data and electronic health records (EHRs), insurers can quickly retrieve and analyze comprehensive medical information side by side with non-medical data, eliminating the need for time-consuming manual collection and review of paper-based records. A streamlined underwriting process that delivers quick results can significantly enhance the customer experience, making it less likely that applicants will abandon the process due to frustration or delays.
Broadening Access
Another aspect of using more data is its potential to broaden access to life insurance, particularly for populations that are traditionally underserved. High-risk applicants, such as those with chronic conditions like diabetes or asthma, often face challenges in obtaining life insurance or are subject to higher premiums and lengthy underwriting processes. However, by considering non-medical data, insurers can identify segments within these populations that present lower-than-expected risks.
The 2024 Life Insurance Mortality Risk Management Study found that among the 50 million individuals studied, 10% have type 2 diabetes. While traditionally not selected for accelerated underwriting due to a high standardized mortality rate (SMR) of 157%, the study found that 10% of these individuals could be good candidates for accelerated underwriting by combining medical and non-medical data. These individuals might otherwise be overlooked for faster processing under traditional models that rely solely on medical data. With more data sources, insurers can offer competitive rates and streamlined processes to applicants who may have been previously categorized as high-risk, thus expanding coverage to a broader demographic.
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Future Implications for the Industry
As the life insurance industry continues to evolve, the integration of medical and non-medical data is poised to play an increasingly important role in shaping the future of underwriting. This aligns with larger trends in the insurance sector, where data-driven decision-making is becoming the norm. Advances in artificial intelligence and machine learning are enhancing the ability to analyze complex data sets, enabling insurers to refine their risk models and make more informed decisions.
Beyond improving the accuracy and efficiency of risk assessments, this approach could also lead to the development of new insurance products tailored to specific risk profiles. Insurers might also use integrated data to identify emerging trends and risks within their portfolios, allowing them to adjust their strategies and offerings. As the industry becomes more data-centric, those who embrace these innovations will be better positioned to meet the evolving needs of their customers and remain competitive in a rapidly changing market.
