Financial institutions face challenges in detecting complex financial crimes like fraud and money laundering, with traditional methods often struggling to keep pace with evolving threats. To address this issue, we introduce Fraud Detect AI, an advanced system that leverages machine learning and AI to automatically track and analyse financial transactions. Fraud Detect AI employs a range of algorithms, including Isolation Forest, Random Forest, Gradient Boosting, Support Vector Machines, Neural Networks, K-Means Clustering, Autoencoders, and Graph Convolutional Networks. This comprehensive approach combines anomaly detection, supervised and unsupervised learning, and network analysis to enhance crime detection capabilities. Comparative analysis demonstrated that Fraud Detect AI outperformed traditional techniques in several key areas: accuracy, with higher precision in identifying fraudulent activities; efficiency, through faster processing of large data volumes; adaptability, showing better detection of new and evolving fraud patterns; and real-time detection, offering potential for immediate fraud identification and prevention. Fraud Detect AI provides a robust framework for financial institutions to enhance their crime detection and prevention strategies, potentially reducing losses and improving regulatory compliance.