Machine learning-based method for monitoring enterprise compliance behaviors

Abstract

This invention discloses a machine learning-based method for monitoring enterprise compliance behaviors, which includes collecting location data of business nodes to obtain an updated location data set; extracting abnormal location groups to obtain dynamic environment change indicators; acquiring compliance rules corresponding to regions in a pre-established regulatory requirement database based on the dynamic environment change indicators to obtain a matched rule set, performing comparison processing on the matched rule set and the location data through a comparison device to obtain an abnormal behavior list; screening entries involving cross-border business from the abnormal behavior list, determining real-time response requirements through time series analysis to obtain a response priority sequence, conducting quantitative assessment on violation risks according to the response priority sequence, and if the quantitative assessment value is higher than a preset assessment value, determining it as a high-risk event and obtaining a final violation risk alarm. The invention realizes the full-process automation from data collection to risk early warning.

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