FRAUD DETECTION IN BANKING TRANSACTIONS WITH THE USE OF ARTIFICIAL INTELLIGENCE AND ANONYMIZED DATA
Keywords:
artificial intelligence, bank fraud, banking transactions, CatBoost, financial transaction analytics, machine learning classification, XGBoost.Abstract
This paper examines whether AI machine-learning classifiers trained on anonymized bank transaction data can effectively predict fraudulent transactions. The study tests H1: at least one classifier’s area under the ROC curve (AUC) > 0.50 against H0: the best classifier’s AUC ≤ 0. 50. Using an anonymized dataset from a U.S.-based commercial bank, we assess an extensive set of classifiers, including tree-based ensembles, probabilistic, distance-based, linear and marginbased learners and a neural network using Orange Data Mining Software. The models were evaluated with stratified 10-fold cross-validation. Multiple models achieved AUC > 0.50, with tree-boosting methods providing the strongest balance between detecting fraud and limiting false alarms. Linear baselines and distance based methods were weak, while SVM produced high recall with operationally costly false positives. Overall, results support H1 and are inconsistent with H0. The study offers a transparent, bank-ready benchmark on anonymized, production-plausible features, and the framework is readily replicable for threshold tuning and governance in financial institutions.
JEL: G21, C45, C52, C55, M42.
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Received: September 18, 2025.
Reviewed: October 27, 2025.
Accepted: December 3, 2025.
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