This study empirically investigates the performance of Elastic Machine Learning, an industrial, unsupervised anomaly detection tool, in the identification of fraudulent behavior in banking transactions. Using AI-generated datasets that were designed to simulate realistic banking environments, the analysis examines three distinct fraud-related scenarios: (i) abnormal associations between a single account and multiple IP addresses, (ii) bursts of cross-border transactions within short time windows, and (iii) unusually high transaction values relative to historical behavior. The results show that the Elastic platform consistently detects anomalous patterns across all examined scenarios by flagging suspicious behavior during the fraud window in real time. This study provides the first empirical assessment of the operational behavior of an industrial, unsupervised anomaly detection platform across multiple fraud-related scenarios in the banking sector, offering practical insights for real-time fraud monitoring and early-warning systems, while supporting institutional resilience and the robustness of the financial system.