Effectiveness of AI-Driven Fraud Detection in Digital Banking

Abstract: The rapid expansion of digital banking has significantly increased the volume of online financial transactions, making the sector a prime target for sophisticated fraudulent activities. Traditional fraud detection methods, relying heavily on static rule-based systems, often fail to adapt to evolving threats and generate high rates of false positives. This paper examines the effectiveness of Artificial Intelligence (AI)-driven approaches in fraud detection within digital banking. AI, through machine learning and deep learning algorithms, enables dynamic analysis of complex transactional patterns, leading to faster, more accurate identification of suspicious activities. By reviewing recent studies, industry applications, and performance metrics, the research highlights how AI enhances fraud detection accuracy, reduces operational costs, and improves customer trust. Challenges such as data privacy, model bias, and the rising complexity of adversarial attacks are also discussed. The findings suggest that while AI significantly strengthens digital banking security, continuous model training, ethical considerations, and regulatory compliance are critical for maintaining long-term effectiveness. The paper concludes by exploring future trends, including explainable AI and federated learning, which promise to further revolutionize fraud detection strategies in the digital banking ecosystem.