In recent years, the rapid advancement of Artificial Intelligence (AI) has transformed financial services, particularly in the domains of credit assessment and fraud prevention. This study examines the implementation and impact of AI-based systems in the credit approval processes of financial institutions operating in Thoothukudi District, Tamil Nadu. Traditional credit evaluation methods often rely on manual analysis and limited data, resulting in delays and higher susceptibility to errors and fraudulent applications. By integrating AI techniques such as machine learning algorithms, predictive analytics, and automated decision-making tools, lenders can enhance both the efficiency and reliability of credit decisions. This research explores how AI models process large datasets—including credit history, transaction patterns, social indicators, and behavioral signals—to yield more accurate creditworthiness scores. The study also investigates the effectiveness of AI-driven fraud detection mechanisms in identifying anomalous activities in real time, reducing financial losses and operational risks. Using a mixed-method approach involving surveys, interviews with credit officers, and performance data from selected banks and non-banking financial companies in Thoothukudi, the findings highlight improvements in decision speed, risk mitigation, and customer satisfaction. The results indicate that AI-integrated credit approval systems significantly reduce the rate of non-performing loans and fraudulent cases compared with traditional methods. Furthermore, challenges such as algorithmic bias, data privacy concerns, and infrastructural limitations are discussed to provide a holistic view of AI adoption in the local context. The study concludes with recommendations for policymakers and financial institutions to promote ethical, transparent, and equitable use of AI in credit assessment and fraud prevention