AI-Driven Fraud Detection Using Ensemble Machine Learning for Secure Digital Banking Transactions
Keywords:
Banking Fraud Detection, Class Imbalance, Credit Card Fraud, Ensemble Learning, Financial Fraud Analytics, Logistic Regression, Machine Learning, Meta-Classifier, Predictive Analytics, Stacking Ensemble, Transaction Analytics, Weighted Majority Voting.Abstract
“Fraud detection in digital banking has been a demanding task due to the growing number of digital banking users. Automated detection is essential due to the large volume of transaction requests. Previously, machine learning techniques based on simple classifiers have been widely used for fraud detection. However, in unbalanced data sets, these models show reduced precision in fraud detection. In this work, an ensemble of four different classifiers predicted bank transaction frauds. Their predictions were combined in three different ways: majority voting, weighted majority voting, and stacking using a meta-classifier. Individual predictions scored F1-scores above 80% except for logistic regression, which achieved a low score of 62.8%. The ensemble based on stacking performed best, obtaining an F1-score 8.5% higher than that of the strongest individual classifier and 3.2% higher than the ensemble based on majority voting. The result demonstrates that the approach is a promising method for banking transaction fraud detection.”
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