AI-Driven Fraud Detection Models in Financial Networks and Digital Security: A Comprehensive Review
Keywords:
fraud detection, artificial intelligence, machine learning, deep learning, financial networks, digital security, anomaly detection, ensemble methodsAbstract
Fraud detection in financial networks and digital security has become increasingly critical with the exponential growth of digital transactions and sophisticated cyber threats. This paper presents a comprehensive review of artificial intelligence (AI) and machine learning (ML) driven fraud detection models developed between 2015 and 2024. We examine various approaches including supervised learning, unsupervised learning, ensemble methods, and deep learning architectures. The review encompasses fraud detection techniques applied to credit card fraud, money laundering, cybersecurity threats, and digital payment systems. Through systematic analysis of 20 recent publications, we identify key challenges such as class imbalance, concept drift, and real-time processing requirements. We also highlight emerging technologies including federated learning, explainable AI (XAI), and graph neural networks as promising directions for next-generation fraud detection systems. The paper concludes with recommendations for practitioners and researchers, emphasizing the importance of hybrid approaches combining multiple techniques for robust fraud detection. Future research should focus on adaptive learning mechanisms, privacy-preserving techniques, and integration of external threat intelligence.
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