Applying Causal Inference to Cyber Fraud Analytics for Proactive Fraud Prevention
Open Access DepositedBeyond Detection
a principled, end-to-end framework uniting causal identification with production-grade fraud modeling
it strengthens precision–recall performance and decision utility, with calibration and cost metrics tracked across operating points. Robustness is assessed via resampling-based uncertainty and diagnostic checks for instability and drift, and it is analyzed on surface segments where targeted controls are expected to reduce loss. Contributions are threefold
and prescriptive guidance that translates causal effects into actionable controls and governance artifacts—shifting security practice from post-hoc detection to anticipatory mitigation.
empirical evidence that calibrated, cost-aware causal models outperform correlational detectors under evolving conditions
Most cyber-fraud programs remain correlation-driven and reactive, degrading under drift, miscalibration, and asymmetric costs. This Praxis advances a proactive alternative
a causal-inference framework that explains why fraud occurs, quantifies intervention effects, and translates model outputs into decision-ready actions. The methodology integrates directed acyclic graphs for admissibility and bias control with counterfactual reasoning and treatment-effect estimation to evaluate “what-if” interventions. Experiments use a synthetic financial dataset designed to represent diverse, high-impact fraud patterns while preserving privacy. The modeling approach highlights causal design that supports identification checks and uplift/heterogeneous-effect analysis. Across baselines, the causalized pipeline improves decision quality rather than classification alone
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