Where AI Can Quietly Distort Captive Decision-Making
This is a series of articles which we focus on the Artificial Intelligence risks in the captive business. Subscribe to get future series of the article.
Prompt & Playbook Bias: When AI Instructions Quietly Shape Captive Decisions
Most AI governance discussions still focus on the model itself: the training data, the weights, the vendor, or the output. We believe that is necessary, but incomplete. In practice, enterprise AI systems operate inside workflows shaped by human-written instructions, templates, summary rules, and hidden operating guidance. Those instructions affect how the model frames the task, what it emphasizes, and how it resolves ambiguity. In that sense, prompts are not just interface text. They are part of the operating environment around the model.
This matters in insurance because current adoption is not centered on fully autonomous underwriting engines. Current AI tools are used as an assistant: drafting, summarization, internal analysis, claims support, reporting support, and other human-supervised workflows. EIOPA’s 2025 survey found that 65% of insurers were already actively using GenAI and another 23% expected to adopt it within three years, with current use concentrated in customer service, claims, and back-office functions. The same survey said governance increasingly requires attention to prompt engineering and outcomes monitoring. That makes prompt and playbook bias a current control issue, not a future one.
A useful case comes from a 2024 mortgage underwriting study using real U.S. HMDA data. The researchers selected 1,000 real 2022 mortgage applications and converted them into 6,000 test cases by holding the loan application constant while varying only applicant race and credit score. They then asked GPT-4 Turbo to perform an underwriting task: approve or deny the loan and assign an interest rate.