Abstract: A computer system for managing a machine learning model that detects potential anomalies in the operation of a complex system is disclosed. In some embodiments, the computer system is programmed to receive sensor signal data originally produced by sensors of the complex system. The sensor signal data can include values for multiple sensor signals at multiple resolutions. The computer system is programmed to train, from given sensor signal data, the machine learning model that comprises one or more transformers, each transformer capturing a set of relationships between signals in a predetermined group of signals. During training, the computer system is programmed to also establish an expected range for an indicator of the relationship. The computer system is programmed to then execute the machine learning model on new sensor signal data and take remedial steps when any computed indicator falls outside the expected range, indicating a potential anomaly in the operation of the complex system.

Abstract: According to an embodiment of the present invention, an automated computer implemented method for detecting one or more data compromises comprises the steps of detecting an activity indicative of a data compromise based at least in part on a compromise detection report involving at least one of merchant compromise report, region compromise report and acquirer compromise report, wherein the compromise detection report is generated by an automated detection algorithm; classifying the activity based on a combination of risk level, size of data compromise and fraud severity; and identifying a specific mitigation response to the activity through an interface. Another embodiment of the present invention determines whether one or more accounts associated with the activity have been probed or tested by a fraudster to determine if the one or more accounts are valid.