This research investigates the potential of machine learning to identify misconduct in banking data, thereby enhancing the reliability and security of financial transactions. The enormous volumes of transaction data that financial institutions receive have made it increasingly challenging for them to detect fraudulent activity due to the exponential expansion of online payment systems and digital banking. Machine learning techniques frequently surpass conventional rule-based systems when it pertains to intricate and perpetually evolving fraud patterns. In this study, a variety of machine learning techniques are implemented to analyze transaction data and detect anomalous patterns. Random forests, decision trees, and neural networks are all examples of such systems. By employing feature selection, data preprocessing, and model training, the proposed method improves identification accuracy and reduces false positive rates. Machine learning algorithms have the potential to identify suspicious financial transactions by revealing patterns and anomalies that were previously unknown, as indicated by experiments. If these concepts are executed, banking will be more secure, clients will have greater confidence in their institutions, and expenses will decrease.