A Practical Taxonomy for Finance-Specific LLM Risk Detection and Monitoring

Large language models (LLMs) are entering financial-services workflows, introducing sector-specific risks that are not adequately addressed by existing general-purpose guardrails. We present a comprehensive, hierarchical taxonomy of LLM prompt risks tailored to finance, covering data exposure, fraudulent and malicious practices, professional advisory overreach, and content/reputation risks. Developed with industry experts and grounded in regulatory standards, our taxonomy enables targeted risk detection and monitoring. We outline its practical application in layered monitoring frameworks and synthetic dataset generation for benchmarking and model training. This approach supports operational governance monitoring in financial institutions and provides a basis for standardized assessment of finance-oriented LLM safeguards.