Machine Learning Approaches for Fraud Detection in Financial Networks

Globalization and rapid advances in digital innovation have greatly increased the complexity of financial networks, increasing their susceptibility to fraud. An estimated 5 trillion in financial losses occur worldwide as a result of traditional fraud detection techniques' inability to keep up with changing fraudulent tactics. In response, this review article examines how artificial intelligence (AI) might be used to detect financial fraud, emphasizing machine learning (ML), deep learning (DL), and hybrid models as revolutionary approaches. AI may identify hidden fraud trends and flexibly adjust to new threats by examining large datasets. Supervised and unsupervised learning, as well as more sophisticated methods like Graph Neural Networks (GNNs), have been very successful in identifying several forms of financial fraud, such as money laundering, identity theft, and payment fraud. This work synthesizes information from a large number of research papers to give a thorough taxonomy of AI-driven fraud detection approaches. It offers an organized framework for comprehending the efficacy of fraud detection methods by methodically classifying them according to how they are used in various forms of fraud. It also looks at how distributed systems, cloud computing, and edge AI enable real-time transaction monitoring and fraud detection. Even while AI greatly increases detection accuracy, lowers operating costs, and improves regulatory compliance, issues including model explainability, data privacy concerns, algorithmic bias, and the dynamic nature of fraud continue to be major obstacles to its wider use. In order to address these issues, our assessment emphasizes the necessity of cooperative efforts between financial institutions, regulators, and technology vendors. Future studies should concentrate on enhancing the AI model's transparency, utilizing federated learning to improve fraud detection skills, and combining AI with blockchain for safe data exchange.