Detecting fraudulent activities in transactions made by customers in financial institutions by analyzing the transactional data. Fraud detection in machine learning involves using advanced algorithms to analyze transactional data and identify patterns that indicate fraudulent activities. The algorithms learn from historical data to detect anomalies and flag suspicious transactions in real-time, preventing financial losses for businesses and protecting customers from identity theft. Machine learning techniques such as clustering, decision trees, and neural networks are commonly used in fraud detection models. The accuracy and efficiency of these models depend on the quality and quantity of the data used to train them, as well as the ability to continually update the models with new data to adapt to evolving fraud patterns.