Identifying credit card fraud transactions is a critical task in maintaining financial security, and anomaly detection algorithms like Isolation Forest and Local Outlier Factor play a crucial role in this process. These algorithms excel in detecting unusual patterns or outliers within a dataset, making them effective for uncovering fraudulent activities that deviate from normal transaction behaviors. Isolation Forest identifies anomalies by isolating them into smaller partitions within the dataset, while Local Outlier Factor computes the local density deviation of data points to identify outliers. When applied to credit card transactions, these algorithms can effectively discern unusual patterns indicative of fraudulent behavior, enabling timely detection and prevention of unauthorized activities. By leveraging machine learning techniques, financial institutions can enhance their ability to safeguard against credit card fraud, providing a robust layer of defense in an increasingly digital and interconnected financial landscape.