Fraud detection systems often face unreliable ground truth due to costly and inconsistent human labeling. This session presents agent-in-the-loop framework, which uses large language models (LLMs) as intelligent labelers to power a continuous learning flywheel. The LLM integrates domain knowledge and historical data to generate high-quality labels, propose new predictive signals, and enhance model retraining. Through agent-in-the-loop feedback and quality controls, the system continually refines its understanding. Attendees will learn how adaptive LLM-ML systems can drive scalable and self-improving fraud detection models.