Rethinking LLMOps for Fraud and AML: Building a Compliance-Grade LLM Serving Stack

ArXi:2605.11232v1 Announce Type: new Fraud detection and anti-money-laundering (AML) compliance are high-value domains for large language models (LLMs), but their serving requirements differ sharply from generic chat workloads. Compliance prompts are often prefix-heavy, schema-constrained, and evidence-rich, combining reusable policy instructions, risk taxonomies, transaction or document context, and short structured outputs such as JSON labels or risk factors.