Machine learning for fraud detection in public sector financial systems
Purpose: Public sector financial systems process large volumes of payments, procurement transactions and grants and remain exposed to fraud, corruption and error. Traditional audit-based controls detect only a small share of irregular transactions and usually do so late. This study examines whether machine learning can improve detection of fraudulent transactions in public sector financial systems and compares model families under conditions resembling real government data, including severe class imbalance and scarce fraud labels. Design/methodology/approach: The study builds a synthetic public-sector transaction dataset of 60,000 records that reproduces documented properties of government payment data, including low fraud prevalence, vendor concentration and period-end clustering. Four models are trained and compared: logistic regression, random forest, XGBoost and isolation forest. Models are evaluated using precision, recall, F1 score, area under the receiver operating characteristic curve and area under the precision-recall curve, since accuracy alone misleads under imbalance. Findings: XGBoost achieved the strongest balance of precision and recall (F1 = 0.762, AUC-ROC = 0.992), followed by random forest (F1 = 0.748). Logistic regression achieved high recall but low precision, producing many false alerts. Isolation forest, which is an unsupervised method, performed reasonably well without labelled fraud, which matters since most agencies lack reliable labels. Amount, vendor age, single-bidder procurement and prior irregularity counts were the strongest predictors. Originality/value: This study contributes a reproducible, coded evaluation framework for public sector fraud analytics and gives practitioners evidence-based guidance on model choice under label scarcity and class imbalance, which are conditions that dominate real government settings. Keywords: Fraud Detection, Machine Learning, Public Sector, Financial Systems, Public Procurement, Anomaly Detection, Government Auditing, XGBoost.