Transforming AI from "Memorizing Questions" to "Solving Problems": Insights from Anti-Money Laundering (AML) Practice on Fine-Tuning
Through an Anti-Money Laundering (AML) case study, this article demonstrates how Taiwan AI Cloud has evolved AI from rote memorization of answers into experts with logical reasoning abilities. The article documents the process from LoRA validation and model distillation to optimization using the CoT (Copy of Thought) and GRPO relative reward modeling, emphasizing that a good model is not formed through a single training iteration, but rather relies on a rigorous pipeline for continuous "management" and evolution. This engineering philosophy proves that only by establishing visible and automated processes can AI be transformed into stable and reliable digital assets in demanding regulatory environments.