Fraud detection in financial services has been a perennial challenge, requiring constant adaptation to ever-evolving landscape of financial crimes. Advances in technology, particularly artificial intelligence (AI) and machine learning (ML), offer new avenues for enhancing detection and prevention of fraud. As fraudsters become more sophisticated, the need for innovative and robust solutions becomes increasingly critical to protect financial institutions and their customers. This paper discusses a literature review that effectively convey use of AI for fraud detection and prevention in banking sector through review of existing literature. Fraud detection using AI in banking swiftly recognize irregularities, analyze patterns, and assign risk scores to customers. In today’s era, fraud is pervasive across various sectors, particularly in financial services. The rise of technologies like AI and big data is revolutionizing fraud detection by leveraging ML to analyze vast amounts of data and identify suspicious activities. Fraudulent activities pose significant threat to various sectors of society, including finance, e-commerce, healthcare, and more. Traditional methods of fraud detection and prevention often fall short in identifying and mitigating sophisticated and rapidly evolving fraud schemes.