Artificial Intelligence: The Secret Weapon Against Financial Fraud

Abstract This study examines the effectiveness of Artificial Intelligence (AI) techniques in detecting financial fraud, while also addressing the key challenges associated with their implementation and use. It presents a systematic review of peer-reviewed literature to evaluate how AI-driven approaches particularly machine learning (ML) and deep learning perform in fraud detection within big data environments. The findings reveal that AI-based systems significantly enhance fraud detection capabilities by enabling real-time analysis and demonstrating greater adaptability to evolving fraud patterns compared to traditional rule-based systems. However, several challenges hinder their optimal performance, including data privacy concerns, vulnerabilities in electronic systems, ethical considerations, and potential algorithmic bias. Additionally, issues related to scalability limit the ability of small and medium-sized enterprises to fully benefit from AI technologies. Despite these constraints, AI remains a powerful tool in combating financial fraud. Addressing these challenges requires improvements in data quality, increased transparency in AI models, and stronger cybersecurity frameworks. Moreover, effective collaboration between policymakers and industry stakeholders is essential to develop regulatory and ethical guidelines that ensure the responsible and efficient use of AI in fraud detection.