Anomaly detection in cross-system operations

A method of anomaly detection in cross-system operations, the method comprising:

• generating, by one or more processors coupled with memory, using one or more models trained with machine learning on data collected from one or more systems of records associated with historical network operations, a plurality of predicted values related to a network operation for an object identifier at a first time interval;
• identifying, by the one or more processors, from the one or more systems of records, a plurality of actual values output responsive to execution of the network operation at the first time interval;
• determining, by the one or more processors, a variance in at least one value of the plurality of actual values based on a comparison of the plurality of actual values and the plurality of predicted values;
• detecting, by the one or more processors, using the one or more models, an anomaly in the variance;
• and
• executing, by the one or more processors, responsive to detection of the anomaly, an action to update the one or more models based on the anomaly.

A non-transitory computer-readable medium storing processor executable instructions that, when executed by one or more processors, cause the one or more processors to:

• generate, using one or more models trained with machine learning on data collected from one or more systems of records associated with historical network operations, a plurality of predicted values related to a network operation for an object identifier at a first time interval;
• identify, from the one or more systems of records, a plurality of actual values output responsive to execution of the network operation at the first time interval;
• determine a variance in at least one value of the plurality of actual values based on a comparison of the plurality of actual values and the plurality of predicted values;
• detect, using the one or more models, an anomaly in the variance;
• and
• execute, responsive to detection of the anomaly, an action to update the one or more models based on the anomaly.