Autonomous accounting anomaly detection engine

Abstract

Novel tools and techniques are provided for detecting and correcting anomalous events in real-time in as users enter data into a general ledger. In various embodiments, a computing system may receive one or more user inputs. The computing system might analyze the one or more user inputs to determine whether there are one or more anomalous transactions associated with the one or more user inputs by obtaining historical information associated with the one or more user inputs and correlating, using one or more machine learning algorithms, the historical information and the one or more user inputs to detect one or more anomalies. Based on the correlation between the historical information and the one or more first user inputs, the computing system might detect at least one anomalous transaction and might generate one or more recommended actions to correct the at least one anomalous transaction.