Financial Fraud Detection with AI: A Machine Learning-Based Approach for Securing Digital Transactions

Abstract

Financial fraud has emerged as one of the most critical challenges in the digital economy, with increasingly sophisticated attack strategies threatening the security of online transactions. This study examined the application of artificial intelligence (AI) and machine learning (ML) models for enhancing fraud detection accuracy and operational efficiency. Six algorithms—Logistic Regression, Decision Tree, Random Forest, XGBoost, Stacking Ensemble, and Graph Neural Network (GNN)—were evaluated using a large, imbalanced dataset of financial transactions. Model performance was assessed based on accuracy, precision, recall, F1-score, and AUC-ROC, with results demonstrating the superiority of ensemble-based and graph-oriented methods. The GNN achieved the highest performance, leveraging its ability to model complex relational structures in transactional data. Feature importance analysis via SHAP values indicated that transaction amount, frequency, and previous fraud history were the most influential predictors. Computational cost analysis revealed a trade-off between model complexity and inference latency, with lightweight models offering faster response times but lower detection capability. The findings suggested that integrating explainable AI and ensemble learning could significantly improve fraud detection while ensuring regulatory compliance and operational transparency. This research contributes to both academic literature and practical financial security systems by offering empirical evidence and actionable insights for the deployment of advanced fraud detection technologies.