This article discusses the challenges and importance of using machine learning in fraud detection, focusing on balancing automation, accuracy, and customer experience in the face of constantly evolving fraud tactics.
This article explores various metrics used to evaluate the performance of classification machine learning models, including precision, recall, F1-score, accuracy, and alert rate. It explains how these metrics are calculated and provides insights into their application in real-world scenarios, particularly in fraud detection.
