Published Paper Details:

Artificial Intelligence (AI) has transformed financial fraud detection by enabling more accurate, scalable, and adaptive systems across sectors such as banking, insurance, and healthcare. This systematic review examines the effectiveness of AI-based approaches in detecting financial fraud and explores the challenges and limitations associated with their implementation. Peer-reviewed studies from major academic databases were systematically analyzed, focusing on machine learning and deep learning techniques used to evaluate the performance of AI-driven fraud detection systems. The results demonstrate that AI significantly enhances real-time fraud detection and improves adaptability to evolving fraud patterns when compared to traditional rule-based methods. Despite these advantages, several challenges hinder widespread adoption, including ethical concerns, algorithmic bias, data privacy risks, system vulnerabilities, and scalability constraints--particularly for smaller organizations. Overall, AI-based fraud detection represents a transformative solution for combating financial fraud; however, realizing its full potential requires improved data quality, the development of explainable AI models, strengthened cybersecurity measures, and updated regulatory frameworks. Collaboration among policymakers and stakeholders is essential to ensure the ethical and effective deployment of AI in fraud detection.