Time to teach insurance to computers

I’ve been noodling on an idea ever since I started working with machine learning (ML) and artificial intelligence (AI) software suites. Due to retirement and outsourcing, insurers are facing a steep drop in institutional knowledge about the industry, their products, and their processes.

This knowledge was acquired over 20-30 years through a close working relationship between the business, IT, and the core systems in use. In many cases, these details are in the heads of a select few service reps, business analysts, and programmers. The documentation may exist, but it requires a base understanding of insurance and corporate history to understand it.

When I think about the processes that led to the possession of seemingly intuitive knowledge, I conclude that it all started with a strong foundation. Many companies encouraged their employees to pursue industry designations and certifications, both for the business and technical staff. These designations are offered by LOMA, LIMRA, FINRA, and the American College. Acquiring these credentials equips the individual with a breadth and depth of knowledge about insurance products, processes, and sales. This is the building block upon which the company-specific knowledge is layered.

Web servers stand inside the Facebook Inc. Prineville Data Center in Prineville, Oregon, U.S., on Monday, April 28, 2014. The Facebook Prineville Data Center features leading energy-efficient technology, including features such as rainwater reclamation, a solar energy installation for providing electricity to the office areas and reuse of heat created by the servers to heat office space. Photographer: Meg Roussos/Bloomberg

My experience in working with ML/AI solutions leads me to believe that the same principles apply. Developing the corpus—a collection of written texts, especially the body of writing on a particular subject—is an important step in machine learning programs. For the insurance industry, a good first step could be to seed the corpus with a broad industry foundation. This is where “teaching” the curriculum for these designation programs could play a large part.

Of course, this type of approach would need to respect the intellectual property of the designated organizations, and it will require creative licensing and partnership arrangements. But, think of what is possible if the starting point for an ML program was on par with a FLMI, CLU, or Series 7 graduate. Then, through thoughtful curation of the existing documentation on products and processes, the company-specific knowledge base can grow.

Sound like science fiction? Companies are using this approach to fill the knowledge gaps in their contact centers. Check out my recently published brief on technology strategies for life insurance contact centers.

This article was reprinted with permission from Novarica

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