Machine learning reduces audit detection risk in 3.3 million public sector general ledger transactions

<title>Abstract</title> <p>Machine learning models for audit anomaly detection are commonly evaluated using proprietary or synthetic datasets, with limited validation against official audit outcomes. This study proposes a four-layer ML framework designed to reduce audit detection risk and evaluates its performance on 3,329,189 general ledger transactions from three consecutive fiscal years (FY2023 to FY2025) of a Mongolian public sector energy utility. The dataset comprises 9,909 account rows and a cumulative debit flow of MNT 16.34 trillion. The proposed framework integrates unsupervised ensemble labeling through Isolation Forest, Z-score analysis, and debit-credit ratio screening, followed by supervised classification with Random Forest, Gradient Boosting, and Decision Tree models. An explainable AI layer maps SHAP feature attributions to specific ISA requirements. Against a simulated 20% MUS baseline, Random Forest achieves F1 = 0.966, AUC = 0.999, and Detection Risk = 2.01%, compared to MUS Detection Risk of 38.05% to 52.7%. McNemar’s test confirms statistically significant superiority (χ² = 1,666.63, p &lt; 0.001; b/c = 107.2). Processing time falls from 310 to 357 hours to 4.5 hours (-98.7%). Temporal holdout yields F1 = 0.955; cross-sector validation produces mean F1 = 0.741. A novel finding is also documented: Benford’s Law digit-distribution anomalies at the transaction level (χ² = 15.51, p &lt; 0.05) are suppressed in account-level aggregates (MAD &lt; 0.006), termed the aggregation masking effect. Validated against official NAO reports at 85 to 90% true-positive alignment, the framework is released as an open-source web application.</p>