For me, language models ("LLMs") have been incredibly valuable and have provided a fresh start in improving processes within the insurance industry. After more than 20 years of technical and strategic work in insurance, I have spent the past two years focusing entirely on AI-augmenting processes and enhancing areas where the industry has previously stood still.
Some of the areas of change I've worked on include operational risk management, analyzing how complex insurance regulations impact the organization and governance documents, and clarifying gaps between direct and reinsurance contracts. What these areas have in common is the need for structured handling and synthesis of large amounts of information, and drawing conclusions through well-founded reasoning. Since exact answers are seldom available, analysis and reasoning are absolutely crucial.
I've seen how language models can relatively quickly streamline and scale these processes, but also that domain knowledge in insurance is absolutely essential for creating real quality. When I work with LLMs, I start by imagining the almost ideal process—as if I had access to a large number of dedicated specialists performing the analysis manually. Then I integrate the language models where they are most effective and where manual analysis is not economically justifiable or possible to perform in a reasonable time. In this way, a process is created that integrates with the current organization and becomes scalable.
Augmenting processes with LLMs requires a structured approach, precise language adapted to the model, and a clear understanding of the goal. Just as when leading people in an organization, vague instructions often lead to failures. LLMs can draw incorrect conclusions, but so do people when instructions are unclear. Therefore, prompt engineering is required—a process where you clearly specify the task for the language model, provide sufficient basis to analyze the issue, and then iteratively adjust to improve response quality.
When designing an extensive multi-step process using LLMs to analyze a regulatory framework's impact on an organization, you realize both the complexity of the task and the satisfaction of understanding and specifying the entire process—from the initial analysis of each article in the regulatory framework to well-motivated proposals for changes for the board's approval.
Do LLMs replace people? Partially, but primarily it's now about augmenting domain experts so they can drive their ideas more effectively. You still need to be able to think, have ideas, and know what needs to be done. Some tasks that many may have enjoyed will disappear, but more people can now advance their ideas in ways they couldn't before. Many of us have always had ideas and could solve problems, and with LLMs in the toolbox, we can now design processes as if we had almost infinite resources—as long as we have domain experts who ensure quality and validate the results.
Please contact me to discuss possibilities!
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