AI Driven Fraud Detection Models in Financial Networks: A Comprehensive Systematic Review

Rapid advancements in digital innovation and globalization has significantly increased the complexity of financial networks, making them more vulnerable to fraud. Traditional fraud detection methods struggle to keep pace with evolving fraudulent strategies, contributing to an estimated global financial loss of $ 5 trillion. In response, this review paper explores the role of artificial intelligence (AI) in financial fraud detection, highlighting machine learning (ML), deep learning (DL), and hybrid models as transformative solutions. By analyzing vast datasets, AI can uncover hidden fraud patterns and dynamically adapt to emerging threats. Techniques such as supervised and unsupervised learning, along with advanced approaches like Graph Neural Networks (GNNs), have proven particularly effective in detecting various types of financial fraud, including payment fraud, identity theft, and money laundering. This paper presents a comprehensive taxonomy of AI-driven fraud detection methodologies, synthesizing insights from a substantial number of research papers. It systematically categorizes fraud detection techniques based on their application in different types of fraud, providing a structured framework to understand their effectiveness. In addition, it examines the role of cloud computing, edge AI, and distributed systems in enabling real-time transaction monitoring and fraud detection. Although AI significantly improves detection accuracy, reduces operational costs, and strengthens regulatory compliance, challenges such as model explainability, data privacy concerns, algorithmic bias, and the dynamic nature of fraud remain critical barriers to widespread adoption. Our review highlights the need for collaborative efforts among financial institutions, regulators, and technology providers to address these challenges. Future research should focus on improving the transparency of the AI model, integrating AI with blockchain for secure data sharing, and leveraging federated learning to enhance fraud detection capabilities. By addressing these challenges, AI can play a pivotal role in securing financial systems, minimizing fraud risks, and fostering cross-industry collaboration for more resilient fraud detection frameworks.