Quality-Aware Simulated Human-in-the-Loop Learning for Card Transaction Fraud: An Information-Theoretic Proof of Concept

This study evaluates simulated single-round label acquisition for anonymised card-transaction fraud within a software-quality framework. A fixed chronological split, logistic regression and histogram gradient boosting, 30 random seeds, five review budgets, and 12 acquisition policies are used with an ideal label oracle. A 67-setting weight-simplex analysis examines the sensitivity and redundancy of the composite uncertainty, risk and threshold-proximity score. Logistic-regression baseline average precision is 0.5945; the highest mean acquisition AP is 0.6093 for entropy plus threshold proximity at a 5% budget, with unchanged recall and higher illustrative total cost. Histogram boosting is substantially more variable across seeds; its highest mean AP among the 12 core policies is 0.3698, compared with a baseline of 0.3140, and the paired seed interval includes zero. The composite score does not consistently dominate simpler policies or retain an optimal weight across budgets. Binary entropy is theoretically rank-equivalent to predictive ambiguity, rather than an independent information source. Retrospective monitoring applies Holm correction over 180 comparisons per classifier and reports KS effect magnitudes and PSI. All six temporal chunks trigger the specified effect-gated alarm, whereas none of 100 empirical same-source control repetitions per classifier does; this control does not establish prospective specificity. Costs are hypothetical relative penalties, SHAP examples are illustrative, and all empirical conclusions are restricted to a single-dataset proof of concept.