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.
New Data, New Processes
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.