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Advancing Machine Learning for Financial Fraud Detection: A Comprehensive Review of Algorithms, Challenges, and Future Directions

H. M. M. N. Herath(1),


(1) University of Kelaniya
Corresponding Author

Abstract


The increasing prevalence of credit card fraud in digital financial transactions necessitates more advanced detection techniques beyond traditional rule-based methods. This study explores the application of machine learning (ML) algorithms in fraud detection, highlighting the effectiveness of models such as Random Forest, LightGBM, and Artificial Neural Networks in identifying fraudulent activities with high accuracy and recall. Additionally, the research examines the role of feature engineering, data augmentation (SMOTE, K-CGAN), and ensemble learning in enhancing fraud detection capabilities. Key challenges, including data privacy concerns, regulatory compliance, and the evolving nature of fraud tactics, are discussed, along with proposed solutions such as federated learning and adaptive fraud detection models. The study also identifies future research directions, emphasizing the integration of deep learning, reinforcement learning, and cross-domain data sources for more effective fraud prevention. By leveraging advanced ML techniques, financial institutions can improve fraud detection accuracy, reduce false positives, and enhance the security of digital transactions while ensuring compliance with privacy regulations.

Keywords


Credit card fraud detection; Data augmentation; Deep learning; Fraud prevention; Machine learning

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