Fraud Detection in Financial Transactions Using Anomaly Detection Techniques: A Cybersecurity Perspective

Financial fraud poses a significant and evolving threat to global digital economies, particularly as financial transactions increasingly shift toward online and mobile platforms. Traditional rule-based and supervised machine learning systems often struggle to identify emerging or unseen fraud patterns, resulting in high false-negative rates and delayed detection. This paper explores anomaly detection as a powerful approach to fraud detection within the broader context of cybersecurity. By identifying deviations from established transaction behavior, anomaly detection techniques can uncover previously unknown fraud schemes with minimal labeled data. The study provides a comprehensive review of state-of-the-art anomaly detection algorithms—including statistical models, isolation forests, one-class support vector machines, autoencoders, and graph neural networks—applied to financial fraud detection. A hybrid anomaly detection framework is proposed that integrates temporal behavior modeling, graph-based relationship analysis, and adaptive learning for real-time fraud identification. Experimental evaluations using benchmark and synthetic financial datasets demonstrate the effectiveness of the proposed framework in reducing false positives while improving recall for fraudulent transactions. Furthermore, the paper discusses practical challenges related to data imbalance, concept drift, explainability, and regulatory compliance in real-world deployment. The findings highlight the importance of anomaly detection as a cornerstone of cybersecurity strategies for financial institutions, enabling proactive, adaptive, and interpretable fraud detection in dynamic financial ecosystems.