Large Language Models (LLMs) are increasingly deployed in high-stakes financial domains, yet their output stability and factual reliability remain central concerns. We present the first systematic benchmark of four prompt governance approaches (Baseline zero-shot, Few-shot, CRISPE, and ACTFIT) across two representative financial tasks: life insurance risk classification and investment portfolio allocation. Using three commercial LLMs (Claude Sonnet 4. 5, GPT-4. 1, and Gemini 2. 5 Flash), we evaluate 60 simulated cases through 2, 160 API calls, measuring accuracy, hallucination rate, cross-run consistency, and a composite Efficiency Score. Evaluation employs two independent LLM judges from different model families (Claude Opus 4. 6 and GPT-4o) with inter-rater agreement of κ = 0. 97 for accuracy, validated by a stratified human sample (n = 98, κ = 0. 82 judge-human). ACTFIT achieves 67. 0% accuracy versus 44. 9% for the baseline (+22. 1pp), with particularly pronounced effects on investment tasks (+46. 1pp). ACTFIT yields the highest Efficiency Score (0. 537 vs. 0. 342 baseline, +56. 8%). GPT-4. 1 combined with ACTFIT reaches the dataset peak: 81. 7% accuracy with 0% hallucination at US 0. 006 per correct response. CRISPE produces a surprising negative finding: accuracy below the baseline (41. 1% vs. 44. 9%) with a 49. 1% hallucination rate, demonstrating that poorly designed governance degrades performance. Our findings suggest that institutional prompt governance offers a cost-effective alternative to fine-tuning for financial applications, with implications for regulatory compliance and operational risk management.
hirbis (Sat,) studied this question.