Label-Free Calibration of Fraud Rule-Based Detection: Addressing Behavior Heterogeneity

Fraud remains a critical and evolving challenge in telecommunications, costing the industry billions annually. In Mobile Virtual Network Operator (MVNO) environments, conventional supervised approaches are limited because fraud labels are scarce or delayed, and outgoing-call behavior is shaped by heterogeneous tariffs. Using a real-world MVNO dataset (9603 subscribers, 1.78 million outgoing CDRs), we derive payment-based segments and confirm statistically significant baseline differences via Kruskal–Wallis tests with Dunn post hoc pairwise comparisons and Benjamini–Hochberg correction. We propose a plan-aware calibration strategy setting interpretable thresholds using segment-wise empirical quantiles. Evaluation employs both operational metrics (activation rates and workload) and two label-free alert quality proxy metrics: multi-rule co-occurrence and activation stability (coefficient of variation). Compared to global calibration, segment-aware calibration reduces the dominant S4 rule activation (5.44% to 4.59% of user-hours) while increasing sensitivity to rare overnight patterns (F6: 0.0017% to 0.0137% of user-days). Experiments confirm improved alert quality, and the robustness of these findings is confirmed by sensitivity analysis across quantile levels and alternative segmentation schemes. Overall, segment-specific calibration yields a more balanced, interpretable, and operationally fair rule-based screening layer suitable for MVNO constraints.