Comparative Review of Confidence and Other Evaluation Metrics in Predictive Modeling for Procurement Fraud Coalition

Procurement fraud, particularly when bidders act together through collusion or coalition schemes, remains a major threat to fair competition in public procurement. Predictive modeling has emerged as a key analytical tool for detecting such behaviors yet choosing appropriate evaluation metrics continues to be a challenge, especially with imbalanced or correlated data. This study applies a structured narrative review supported by a comparative analysis to examine commonly used evaluation metrics—Accuracy, Precision, Recall, F1-score, and AUC-ROC—in relation to the rule-based Confidence metric derived from association rule mining. The findings reveal that while traditional classification metrics are effective for general predictive tasks, they often fail to capture relational and co-occurrence patterns that characterize coalition fraud. In contrast, Confidence demonstrates higher interpretability and contextual relevance for detecting collusive behaviors among suppliers. The study highlights the potential of hybrid evaluation frameworks that combine classification and rule-based measures to improve fraud detection accuracy and explainability. This approach contributes to advancing predictive modeling, procurement analytics, and coalition detection by emphasizing metrics that balance performance, interpretability, and real-world applicability.