Application of Artificial Intelligence in Fraud Detection in the Banking Sector

Abstract

The rapid digitization of banking services has increased the susceptibility of financial systems to fraudulent activities. In response, Artificial Intelligence (AI) has emerged as a powerful tool in detecting and preventing fraud in the banking sector. This paper explores the various AI-driven techniques such as machine learning algorithms, neural networks, and natural language processing used to analyze vast volumes of transactional data in real-time. These technologies enable the identification of suspicious patterns, anomaly detection, and predictive risk assessment, significantly enhancing the speed and accuracy of fraud detection systems. Furthermore, AI models continuously learn from evolving fraud tactics, making them more adaptive and robust than traditional rule-based systems. Despite its advantages, the adoption of AI also presents challenges related to data privacy, algorithmic bias, and regulatory compliance. This study highlights the current applications, benefits, limitations, and prospects of AI in fraud detection, aiming to contribute to the development of more secure and intelligent banking ecosystems.