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Financial fraud, which involves fraudulent practices to acquire financial gains, has recently become a major issue in businesses and organizations. It is inefficient, expensive, and time-consuming to discover fraudulent activities through manual verifications and inspections. The intelligent detection of fraudulent transactions is made possible by artificial intelligence through the evaluation of enormous amounts of financial data. Key components for ensuring operational integrity and limiting financial losses in the financial services business include fraud detection and risk assessment. Due to the increasing complexity of fraud schemes, traditional techniques of detection that depend on static rules and historical data are no longer adequate. In order to better detect fraud and evaluate risk in the financial services sector, this study explores the application of predictive analytics and machine learning (ML). Real-time data and adaptive algorithms are used to evaluate the performance of ML techniques such as supervised learning, unsupervised learning, and ensemble methods in detecting fraudulent actions. The results show a considerable improvement in detection accuracy and risk assessment over older methods. This paper also explores the possible obstacles of deploying these technologies, such as data privacy concerns, interpretability, and the need for ongoing model training.