Accurate credit risk assessment and fraud detection are crucial for financial institutions to mitigate losses and maintain stability. Traditional methods relying on rules, thresholds, and limited data sources face limitations in handling evolving fraud tactics and complex risk patterns. Artificial intelligence (AI) techniques, such as machine learning, deep learning, and predictive analytics, offer promising solutions by leveraging large and diverse data sources to uncover hidden insights. This paper explores traditional and AI-driven approaches to credit risk assessment and fraud detection, highlighting their respective strengths, limitations, and real-world case studies. It further explores the critical success factors and challenges associated with AI adoption in banking and finance, emphasizing the importance of data quality, model accuracy, scalability, and responsible governance. Ultimately, by strategically integrating AI while addressing technical and ethical considerations, financial institutions can revolutionize risk management, optimize decision-making, and foster a more resilient financial ecosystem