This research explores the application of artificial intelligence (AI) in detecting fraudulent banking transactions, emphasizing the importance of machine learning algorithms in enhancing security and trust in the financial ecosystem. The study investigates various machine learning models, including Random Forest, K-Nearest Neighbor (KNN), Naïve Bayes, Decision Trees, and Logistic Regression, to analyze and identify fraudulent activities. The research highlights the effectiveness of these models in detecting anomalies and improving the accuracy of fraud detection. Logistic Regression and Decision Trees demonstrated high accuracy and Area Under the Curve (AUC) values, indicating their superior performance in this context. The study also addresses the challenges associated with traditional rule-based fraud detection methods, which struggle to adapt to the evolving nature of cyber threats. By leveraging machine learning, the research aims to develop more adaptive and efficient approaches to fraud detection, contributing to the scientific understanding of fraudulent banking transactions. The findings underscore the potential of AI in combating banking fraud, with logistic regression emerging as the top-performing algorithm in this study. The research emphasizes the need for continuous innovation and the integration of advanced technologies to ensure the security and integrity of financial transactions in the digital age.This research explores the application of artificial intelligence (AI) in detecting fraudulent banking transactions, emphasizing the importance of machine learning algorithms in enhancing security and trust in the financial ecosystem. The study investigates various machine learning models, including Random Forest, K-Nearest Neighbor (KNN), Naïve Bayes, Decision Trees, and Logistic Regression, to analyze and identify fraudulent activities. The research highlights the effectiveness of these models in detecting anomalies and improving the accuracy of fraud detection. Logistic Regression and Decision Trees demonstrated high accuracy and Area Under the Curve (AUC) values, indicating their superior performance in this context. The study also addresses the challenges associated with traditional rule-based fraud detection methods, which struggle to adapt to the evolving nature of cyber threats. By leveraging machine learning, the research aims to develop more adaptive and efficient approaches to fraud detection, contributing to the scientific understanding of fraudulent banking transactions. The findings underscore the potential of AI in combating banking fraud, with logistic regression emerging as the top-performing algorithm in this study. The research emphasizes the need for continuous innovation and the integration of advanced technologies to ensure the security and integrity of financial transactions in the digital age.