Accelerated underwriting can speed the insurance process and provide better coverage and pricing options for applicants. In , 69色情片's Catie Muccigrosso shares seven factors insurers should evaluate and align to effectively implement this underwriting approach.
Accelerated underwriting (AU), as a fluidless alternative to traditional full underwriting, can speed the insurance process and provide better coverage and pricing options for applicants. This evolving approach applies applicant data in a mortality-neutral way, acting as a proxy for paramedical visits and lab work necessary in full underwriting.
The risk assessments in AU should ideally yield the same underwriting decisions as would full underwriting. The principal risk in using AU, however, is mortality slippage, i.e., the gap in mortality experience between accelerated policies and a fully underwritten baseline.
AU first emerged about a decade ago but became more popular during the early months of the COVID-19 pandemic due to lockdowns and social distancing requirements. Today鈥檚 AU programs are synergistically structured around specific combinations of data sources.
Insurers must evaluate and align several elements to effectively implement this underwriting approach: company goals, policy characteristics, risk appetite, target market(s), data sources, data vendors, and overall program cost.
An insurer鈥檚 philosophy, structure, size, book of business, risk tolerance, and other characteristics impact an AU program. Ultimately, a company鈥檚 decision to use AU must balance mortality protection with underwriting speed, placement, and convenience. Conservative AU program goals might dictate waiting for an attending physician statement (APS) to determine mortality, or prescreening applicants more carefully with additional requirements. If a speedier process is the goal, then program choices might tend to skew to fewer requirements obtainable in a short time, which could impact the potential mortality slippage.
Setting up an AU program begins with determining which products, features, and face amounts will reap the most significant returns from its use or bolster the insurer鈥檚 competitive advantage. For example, higher face amounts 鈥 those above $2 million 鈥 may need a more complex underwriting process to prevent mortality slippage. In contrast, lower face amounts may lend themselves to AU but require appropriate safeguards to achieve goals and limit mortality slippage.
An insurance company鈥檚 risk appetite often directly impacts its AU strategy. Determining which tools and data are necessary to assess risk confidently means including factors such as the intended customer mix, growth goals, and comfort with mortality slippage.
Each insurer sets applicant eligibility requirements and exclusions in order to control risk selection based on specific target markets. Doing so involves balancing the risk or growth of a book of business in accordance with applicant characteristics such as income, occupation, location, or age. Third-party data used in AU can also be leveraged to cross-sell and upsell life insurance products tailored to particular consumers.
Adequate and predictive alternative data are necessary for mortality-neutral risk assessment in order to protect against mortality slippage. Such data sources can include attending physician statements, credit information, electronic health records, healthcare claims data, lab results, and patient portals.
If not used judiciously, however, the data can lead to applicant misclassifications and significant variances in mortality costs. To determine data adequacy or protectiveness, insurers should factor in the 鈥渉it rate,鈥 i.e., the likelihood a type of applicant will appear on a vendor鈥檚 database. For instance, middle-to-upper-class applicants are more likely to have robust medical records and a higher hit rate for electronic health records. In contrast, credit data might have a higher hit rate for applicants between ages 25 to 70.
Choosing the appropriate vendors for alternative data is also important and should take several factors into account, including availability, cost, frequency, recency, information variability and completeness, and ease of acquisition and use. A prescription data vendor that combines information from large national pharmacy chains, for instance, may be more likely to offer clues, such as prescription compliance rates, to an applicant鈥檚 health and self-care. However, the value of alternative data sources can vary by case. For example, one applicant鈥檚 lab records may include a complete blood panel, while another鈥檚 might only show a flu test.
AU should be less expensive than full underwriting, which means acquiring the data should not cost more than conducting medical exams and labs. The savings from lower expenses associated with alternative data often offsets potential mortality slippage. Therefore, it is crucial to examine the impact of potential mortality slippage, evidence expense, underwriter time, and cost per case to ensure that the program is profitable and preserves overall portfolio mortality.
AU can be a powerful approach, but it should only be considered in cases where it makes sound business sense. As more relevant customer and market data becomes available, AU will continue to evolve. How AU impacts the overall customer experience and insurers鈥 expense management will likely guide future development. Ultimately, it is up to the industry to thoroughly research available data sources to create better and more sustainable long-term AU solutions that both improve the customer journey and preserve mortality risk principles.
