In this ThinkAdvisor article, RGA's Catie Muccigrosso explains why, as accelerated underwriting evolves, insurers will benefit by adjusting their underwriting manuals' traditional full underwriting guidelines to ensure appropriate use.
The COVID-19 pandemic transformed accelerated underwriting (AU) from a novel to a preferred approach. As it evolves, insurers would be advised to consider adjustments to their underwriting manuals to reflect this new risk assessment landscape.
AU offers several advantages and conveniences for both life insurers and applicants. Unlike full underwriting (FUW), AU’s alternative data-based process does not require vitals and fluids to be collected by a medical examiner. It also reduces evaluation and testing costs.
AU relies on a fast-growing range of health and lifestyle data from multiple sources that differ from information collected in traditional FUW processes.
To work most effectively, the AU process requires support through underwriting manual guidelines that incorporate analysis of the data and assessment of new types of information items into the risk assessment.
Since digital underwriting evidence data can vary in structure depending on the source, guidelines can build an appropriate framework and context for their use. Guidelines can also support the evaluation of applications based on an insurer’s risk appetite, policy characteristics, and other critical factors that influence rating and pricing. Finally, they can also simplify deciding whether an applicant qualifies for AU or should be fully underwritten.
When developing new guidelines, insurers need to consider several elements. Guidelines should balance risk assessment with customer experience to ensure the application process is simple. Rules should support a desirable straight-through rate for automated processing. Done properly, strong risk assessment guidelines and rules that provide a reasonable straight-through rate are highly likely to yield better applicant placement rates.
Writing new AU guidelines requires a change in mindset to account for the significant differences between the medical evidence used for FUW and the digital underwriting data used for AU. Medical evidence, such as lipid panels, offers numerical values for assessing risk. Digital underwriting evidence, when applied to AU, works differently: it deploys various data points and application information in combinations that generate similar value.
Establishing a more rigorous underwriting framework that utilizes digital data to enable lasting and effective change will be critical for success. It starts with observing the in-market experience of each new data source and new analytical tools to identify programs and features worth pursuing and maintaining. Determining the digital underwriting evidence that is the most protective, helpful, and best serves the market’s needs can lay the foundation for building targeted risk assessment and pricing guidelines.
As accelerated underwriting evolves, insurers would be advised to consider adjustments to their underwriting manuals to reflect this new risk assessment landscape.
Further, insurers should review each impairment to determine cover eligibility and pricing. Since digital evidence can yield a different view of an applicant than traditional evidence, underwriting rules may require certain adjustments. For instance, clinical labs may provide a history of A1c readings, but may not include a reading taken within the last six months. Insurance lab panels may provide an inverse view.
Therefore, to achieve a similar conclusion as an FUW assessment, guidelines must be adjusted to gain the most benefit from available data sources.
As the availability of new data sources continues to expand, insurers need to be able to accept applicant health and lifestyle data regardless of its form and then craft guidelines so that automated processes can use the information objectively.
Credit score vendors, for example, often do not provide their information uniformly: the metrics themselves, as well as how they are presented, can differ. Insurers, therefore, must seek to adjust their own guidelines to accommodate such nonuniformity in order to accurately assess the data provided and move the application through the underwriting process efficiently.
Accurately rating applicants, especially those with impairments, depends on a combination of robust and germane data sources. Guidelines can dictate the layers of additional data sources to fill potential information gaps to enhance risk assessment.
For instance, since available digital data does not always provide an applicant’s blood pressure reading, an underwriter may need to rely on other sources of information. Coupling appropriate application questions with relevant data sources, such as a prescription check, can help an underwriter determine whether blood pressure is well-controlled by other means, such as recent changes in medication dosage or type.
As AU evolves, insurers will benefit by adjusting their underwriting manuals’ traditional FUW guidelines to assure appropriate use. Because AU will continue to grow and expand as new datasets emerge, it is imperative that guidelines continue to evolve as well.
