Kaya K.Sayar A.Memis E.C.Ozlem S.Ertugrul S.Cakar T.2026-03-052026-03-0520252521-1641https://doi.org/10.1109/UBMK67458.2025.11206942https://hdl.handle.net/20.500.11779/3231Bounced checks result in direct monetary losses. Traditional rule-based systems cannot adapt to new patterns and lack flexibility. In this study, we used a large and imbalanced check dataset with customer profiles, credit limits, and historical check outcomes. We applied feature engineering emphasizing time-based transaction patterns, extensive clustering, anomaly detection, and inflation adjustment. We trained six models each for two datasets, which are undersampled to handle class imbalance: Logistic Regression, Random Forest, XGBoost, LightGBM, Extra Trees, and CatBoost. The best performing model, CatBoost, achieved macro F1 scores of 88.5 percent on individual checks dataset with a gross sunk rate of 4.92 percent, and 91.7 percent on corporate checks dataset with a gross sunk rate of 4.28 percent. These results show the model can identify checks most likely to bounce before granting and maintain a low gross sunk rate overall. This study presents a data-driven machine learning solution that enables financial companies to predict and prevent bounced checks before they occur. © 2025 IEEE.eninfo:eu-repo/semantics/closedAccessBounced Check PredictionClass ImbalanceFeature EngineeringMachine LearningRisk ManagementTransactional AnalysisConference Object10.1109/UBMK67458.2025.112069422-s2.0-105030881742