The integration of Artificial Intelligence (AI) into the Indian banking and financial sector has revolutionized fraud detection and prevention strategies. As digital transactions surge, financial frauds such as identity theft, phishing, and cybercrimes have become increasingly sophisticated. Traditional fraud detection methods often fail to identify evolving fraudulent patterns effectively. AI-driven solutions, including machine learning (ML), natural language processing (NLP), and predictive analytics, provide enhanced security by detecting anomalies and preventing fraud in real time. This research delves into AI’s role in safeguarding financial transactions in India by analysing its effectiveness, challenges, and implementation strategies. A mixed-method approach is used, incorporating industry reports, case studies, and expert interviews. Findings indicate a substantial decline in fraudulent activities in banks that have adopted AI-driven solutions, improved transaction monitoring, and heightened customer trust. However, challenges such as regulatory compliance, data privacy concerns, and high implementation costs persist. Addressing these issues requires a balanced approach that combines AI-driven innovations with robust security frameworks. The study concludes with recommendations to enhance AI adoption in financial fraud prevention, ensuring a secure and trustworthy banking environment in India.