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This arXiv paper introduces a role-aware Decider-Supervisor framework combining centralized ML, a FedAvg-trained federated meta-model, and Base/QLoRA LLM variants with blockchain auditability, finding that AI supervision only reduces fraud-decision errors under specific roles, calibration, and traffic conditions rather than by default.