The integration of advanced Artificial Intelligence (AI) algorithms in fintech fraud detection has significantly enhanced the accuracy, efficiency, and adaptability of fraud detection systems. This study explores the transformative potential of AI in detecting and preventing financial fraud, emphasizing the role of machine learning, deep learning, and natural language processing in improving fraud detection capabilities. The research highlights the effectiveness of AI in identifying complex fraud patterns, reducing false positives and negatives, and enabling real-time fraud prevention. The methodology involves the deployment of various AI algorithms on extensive datasets of financial transactions, with a focus on evaluating their performance in terms of accuracy, efficiency, and scalability. Comparative analysis reveals that deep learning models, particularly those based on neural networks, outperform traditional machine learning models in detecting sophisticated fraud. Additionally, the integration of natural language processing allows for the analysis of unstructured data, further enhancing the detection capabilities of AI systems. Key findings indicate that AI-based fraud detection models offer superior performance compared to conventional methods, providing fintech platforms with a more dynamic and predictive approach to fraud prevention. The study also addresses the challenges associated with AI implementation, including data privacy, ethical considerations, and algorithmic bias, emphasizing the need for transparent and equitable AI practices. Overall, this research underscores the critical role of AI in revolutionizing fintech fraud detection, offering practical insights for fintech companies seeking to enhance their security measures and protect their operations against financial fraud. The findings contribute to the academic discourse on financial security and highlight the potential of AI to transform the future of fraud detection in the digital financial ecosystem.The integration of advanced Artificial Intelligence (AI) algorithms in fintech fraud detection has significantly enhanced the accuracy, efficiency, and adaptability of fraud detection systems. This study explores the transformative potential of AI in detecting and preventing financial fraud, emphasizing the role of machine learning, deep learning, and natural language processing in improving fraud detection capabilities. The research highlights the effectiveness of AI in identifying complex fraud patterns, reducing false positives and negatives, and enabling real-time fraud prevention. The methodology involves the deployment of various AI algorithms on extensive datasets of financial transactions, with a focus on evaluating their performance in terms of accuracy, efficiency, and scalability. Comparative analysis reveals that deep learning models, particularly those based on neural networks, outperform traditional machine learning models in detecting sophisticated fraud. Additionally, the integration of natural language processing allows for the analysis of unstructured data, further enhancing the detection capabilities of AI systems. Key findings indicate that AI-based fraud detection models offer superior performance compared to conventional methods, providing fintech platforms with a more dynamic and predictive approach to fraud prevention. The study also addresses the challenges associated with AI implementation, including data privacy, ethical considerations, and algorithmic bias, emphasizing the need for transparent and equitable AI practices. Overall, this research underscores the critical role of AI in revolutionizing fintech fraud detection, offering practical insights for fintech companies seeking to enhance their security measures and protect their operations against financial fraud. The findings contribute to the academic discourse on financial security and highlight the potential of AI to transform the future of fraud detection in the digital financial ecosystem.