Abstract: Systems and methods of the present disclosure enable a processor to automatically detect anomalous user-specified data by receiving an electronic activity verification associated with an electronic activity of a user account, including a value associated with an electronic activity, and a user-specified value indicative of an additional value specified by a user for the electronic activity. The processor generates a feature vector including the verified value and the user-specified value and utilizes an anomalous attribute classification model to ingest the feature vector to determine an anomaly classification based on learned model parameters. The processor generates a dispute graphical user interface (GUI) including an alert message and a dispute interface element, that upon a user interaction causes an electronic request to dispute the electronic activity verification to prevent an execution of the electronic activity.

Abstract: Systems and methods of the present disclosure enable a processor to automatically detect duplicate data entries by receiving data entries associated with a user, where each data entry includes a value, a time, an entity identifier, and a location. Pairs of similar data entries are determined by matching the entity identifier and the location pairs data entries. Candidate duplicate data entries are determined based on a proximity in time between data entries of the similar data entries. For each candidate duplicate data entry, a feature vector is generated including the entity identifier, location, value and time, and each feature vector is submitted to a duplicate classification model to automatically determine duplicate data entries from the candidate duplicate data entries, the duplicate classification model being trained according to a historical dispute entries.

Abstract: Systems and methods of the present disclosure enable a processor to automatically detect duplicate data entries by receiving data entries associated with a user, where each data entry includes a value, a time, an entity identifier, and a location. Pairs of similar data entries are determined by matching the entity identifier and the location pairs data entries. Candidate duplicate data entries are determined based on a proximity in time between data entries of the similar data entries. For each candidate duplicate data entry, a feature vector is generated including the entity identifier, location, value and time, and each feature vector is submitted to a duplicate classification model to automatically determine duplicate data entries from the candidate duplicate data entries, the duplicate classification model being trained according to a historical dispute entries.

Abstract: Systems and methods of the present disclosure enable a processor to automatically predict a sequence of recurring data entries by accessing a history of electronic activity and executing a recurring entry classifier model to generate a library of recognized recurring data entries, where each recognized recurring data entry in the library includes: a precursor period associated with a precursor data entry, a recurrence period associated with a recurring value, and a recurring entity identifier. An electronic activity data entry is received and identified as preceding a recurring data entry based on the electronic activity value being a nominal electronic activity value. The electronic activity data entry is matched to a recognized recurring data entry in the library using the entity identifier. The processor notifies a user of the matching sequence of recurring data entries as a sequence of recurring data entries to commence after the precursor period.

Abstract: Systems and methods of the present disclosure enable a processor to automatically detect duplicate data entries by receiving data entries associated with a user, where each data entry includes a value, a time, an entity identifier, and a location. Pairs of similar data entries are determined by matching the entity identifier and the location pairs data entries. Candidate duplicate data entries are determined based on a proximity in time between data entries of the similar data entries. For each candidate duplicate data entry, a feature vector is generated including the entity identifier, location, value and time, and each feature vector is submitted to a duplicate classification model to automatically determine duplicate data entries from the candidate duplicate data entries, the duplicate classification model being trained according to a historical dispute entries.

Abstract: Systems and methods of the present disclosure enable a processor to automatically detect anomalous user-specified data by receiving an electronic activity verification associated with an electronic activity of a user account, including a value associated with an electronic activity, and a user-specified value indicative of an additional value specified by a user for the electronic activity. The processor generates a feature vector including the verified value and the user-specified value and utilizes an anomalous attribute classification model to ingest the feature vector to determine an anomaly classification based on learned model parameters. The processor generates a dispute graphical user interface (GUI) including an alert message and a dispute interface element, that upon a user interaction causes an electronic request to dispute the electronic activity verification to prevent an execution of the electronic activity.