In this article, originally published in , RGA鈥檚 Dr. Dave Rengachary explains how the future of digital underwriting begins with creating real value today.
For years the life insurance industry has endeavored 鈥 and often struggled 鈥 to digitize and automate the complex process of facultative underwriting.
As data has increased and technology has advanced, we have been inundated with new tools promising to revolutionize the underwriting process. Despite significant progress, the game-changing transformation promised by all the hype has yet to fully materialize.
With recent advances in artificial intelligence (AI), the hype has shifted into overdrive. And while this is indeed an exciting time and we should enthusiastically pursue the many opportunities presented by AI and its accompanying army of abbreviations 鈥 OCR, NLP, LLM, etc. 鈥 we should do so with experience as a guide. We need to look past the buzzwords to get to what really matters: delivering value. The long-awaited transformation seems close at hand, but lasting progress doesn鈥檛 happen overnight; it takes ongoing micro-advances across a range of processes and applications.
Here are four key areas where practical, yet future-focused, steps toward digital underwriting optimization can start delivering real value today:
Efficiencies created through digital underwriting can speed processes, reduce price, and expand customer acquisition. As the flow of data increases and that data becomes more complex, success centers on converting and combining input from both structured (lab panels, etc.) and unstructured (up to 80% of medical records) data sources into ready-to-evaluate digital output. Before getting started, it is critical to ask, 鈥淲ho is using the data, and what do they need it for?鈥 Answering this question helps focus resources, no matter how limited, on the most impactful opportunities amid a sea of possibilities.
In most companies, underwriting and actuarial systems are maintained separately. As a result, after an underwriting decision is made, that rich underwriting information tends to be lost, eliminating its ability to help generate mortality insights. Digitizing underwriting files can unlock a wealth of viable data and, through advanced analytics, help actuaries identify the true drivers of mortality experience.
By pulling out a pool of applicants, digitizing their underwriting evidence and applications, and analyzing the resulting digital summary, (re)insurers can determine if the summary supports the underwriting decision. If not, they can evaluate whether this inconsistency is due to additional impairments, the intricacy of comorbidities, a lack of experience, or other factors. Most importantly, they now have the data to build those factors into rules and models.
Fully automated underwriting requires considering the various evidences available to assess a person鈥檚 individual risk. When looking across this collection of data 鈥 electronic health records and beyond 鈥 the challenge is to create a longitudinal view that allows utilization of all evidences together as part of a holistic evaluation. However, evidences from different vendors in different proprietary formats demand separate rules and decision engines and the systems necessary to process the data together.
Insurers are therefore spending valuable time and resources building technologies to handle different iterations of the same information. The logical next step: working with industry partners to develop shared standards and a common language to eliminate redundancies and expand data utilization.
Generative AI has arrived, and it is here to stay. Among other underwriting applications, its ability to scan pages and pages of documentation for relevant content and instantly produce actionable information 鈥 which might take hours for a human underwriter 鈥 is truly remarkable. As an example, RGA is partnering with Digital Owl, an AI-powered platform, to quickly transform complex medical records into underwriting insights.
Yet even this apparent game-changer must be approached prudently, through micro-advances directed at delivering real value. In this way, progress is grounded in proven success. As we move forward one step at a time, however, we must also continue to think big. We are just scratching the surface of the potential for AI and related technologies, and we鈥檙e going to discover new opportunities to derive substantive insights in ways we haven鈥檛 even conceived of yet. We need to be open to reimagining how we approach everything we do.
The future of digital underwriting may be closer than we realize, and we鈥檒l get there sooner by focusing on the challenges of the present.
Learn how RGA is leading the way in the use of data analytics and technology in underwriting.
