The COVID-19 pandemic introduced new challenges and opportunities for life insurers. Deaths from SARS-CoV-2 not only pressured benefit costs, but also consumer demand for life insurance products.
Meanwhile, conducting medical exams and blood tests 鈥 the mainstays for procuring underwriting information 鈥 became nearly impossible due to public policy intended to quell the spread of the novel coronavirus.
Life insurers needed to pivot 鈥 and fast. Thanks to accelerated or 鈥渇luidless鈥 underwriting, the industry learned it was more agile than previously realized. Moving quickly and carefully, life insurers reconsidered previously underused data sources and already-established processes and systems to meet customer demand, discover new products, and ensure solvency in a sea of present and future unknowns. Here are some lessons life insurers have learned.
Lesson 1: Digital health data is useful for accelerated underwriting.
Digital health data can potentially be used in lieu of traditional labs and paramedical exams. Incorporating alternative evidence from sources such as electronic health records (EHR), clinical labs, and medical claims data can help improve consumer experience and accelerate the underwriting process.
However, the data has limitations. Digital health records generally do not provide the same level of protective value as traditional labs. In addition, they often lack uniformity and structure. To bolster automation, this hurdle must be overcome.
Lesson 2: Accelerated underwriting requires an infrastructure that brings together data from disparate sources in an understandable way.
If the collection process is not set up appropriately, underwriters may face a deluge of information, making it difficult to identify relevant data.
Accelerated underwriting, which is designed to evolve, offers a framework for quickening the process of assessing mortality risk for writing policies by adjusting underwriting rules and models. By setting up a data flow and system that pulls in the information from multiple sources and synthesizes it for underwriter review, alternative data is becoming much more valuable to the underwriting process.
Lesson 3: Accelerated underwriting should be used only where it makes sense.
Determining relevance of alternative data could depend on the applicant, life insurance product, insurer鈥檚 underwriting appetite, and other factors. The value of an alternative data source varies case by case. For example, one applicant鈥檚 lab records may include a full blood panel, while another person鈥檚 may provide only a flu test.
Lesson 4: Creating guidelines for risk assessment and pricing is necessary to move forward strategically.
Alternative underwriting programs are unique to each insurer, but enough commonalities exist to apply valuable evaluation tools for multiple applications.
Setting up a more rigorous framework for permanent change starts with observing in-market results to identify programs and program features worth maintaining. Determining which alternative tools are the most protective and most usable while best serving the market can provide the basis for creating guidelines for risk assessment and pricing.
Lesson 5: No matter how much underwriting is automated and fine-tuned with additional data and models, underwriters remain critically important.
Underwriters can evaluate information and see the big picture for more complex risks like no machine can.
There is nothing like a real-world test to realize an industry鈥檚 strengths and weaknesses. The complications introduced by COVID-19 affirmed that the life insurance industry has the financial strength to withstand a major market disruption, the agility to adapt to an unexpected crisis, and the tools of innovation necessary to better serve its customers in the future.
Reprinted with the permission of .
