MEDT: Delayed-Feedback Expert Selection for Streaming Fraud Detection

Streaming fraud detection must adapt to evolving transaction behavior while recent labels may remain unavailable under delayed supervision. Earlier fraud characteristics may persist, coexist, or reappear, creating the problem of deciding which retained historical models should participate before the newest supervision arrives. We propose Mixed Expert Decision Trees (MEDT), which retains temporally differentiated experts, reassesses predictive competence from observable delayed feedback, and selects high-scoring eligible experts for committee prediction. A frozen historical anchor provides a standing reference, and committee probabilities are combined through anchor-stabilized probability fusion. On the IEEE-CIS-based W26 controlled-perturbation stream, MEDT achieves a PR-AUC of 0.4628, 17.0% above the strongest evaluated online baseline. Removing the temporal-expert component reduces PR-AUC by 0.0436, while replacing soft fusion with hard voting reduces it by 0.1023; both contrasts have Holm-corrected p<0.05 in the planned within-stream paired tests. These results support the temporal-expert component as implemented and soft probability fusion, without establishing a uniquely optimal competence or selection rule.