A Review on Financial Fraud Detection: Techniques, Challenges, Solutions, and Perspectives

Financial fraud is one of the most serious criminal activities, resulting in losses exceeding hundreds of billions of dollars each year. Most existing fraud detection frameworks still use static rule sets or traditional Machine Learning (ML) models, which fail with decentralized systems characterized by anonymity and dynamic behavior. Despite recent developments, the deployment of advanced Artificial Intelligence (AI) techniques, such as Graph Neural Networks (GNNs), anomaly detection, and generative models, remains underexplored in the field of fraud detection. This study conducts a systematic comparative review of sixteen scholarly articles focused on the use of AI for financial fraud detection. By classifying the selected studies into three main categories-transactional, behavioral, and corporate financial fraud-and evaluating them across nine analytical dimensions, the research underscores the methodological diversity, key strengths, and limitations of current AI-driven detection approaches. The integration of deep learning and hybrid methodologies, especially within behavioral and transactional fraud contexts, has shown clear advantages over traditional techniques when tested on real-world datasets. These approaches yield notable enhancements in detection accuracy, robustness, and adaptability, thereby underscoring the efficacy of hybrid AI models in designing reliable and resilient fraud detection systems tailored to the complexities of modern financial environments.