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: Methods, systems, devices, and computer-readable media for detecting multi-party events and transactions are provided. User data may be monitored to detect data associated with an event involving multiple individuals, such as by identifying transaction data associated with certain types of merchants and/or scheduling, calendar, or correspondence data indicative of an event. The data may be further analyzed to identify a date, location, and/or parties associated with the event. A multi-party event may be generated. The user data may continue to be monitored to identify transactions associated with multiple parties and occurring during a time and/or at a location of the event. At a conclusion of the event, the transactions may be aggregated and an optimal payment scheme may be determined for settlement of the transactions between the parties. In accordance with the determined payment scheme, delegation of portions of the aggregated transactions may be initiated for settlement amongst the parties.

Abstract: Methods, systems, devices, and computer-readable media for detecting multi-party events and transactions are provided. User data may be monitored to detect data associated with an event involving multiple individuals, such as by identifying transaction data associated with certain types of merchants and/or scheduling, calendar, or correspondence data indicative of an event. The data may be further analyzed to identify a date, location, and/or parties associated with the event. A multi-party event may be generated. The user data may continue to be monitored to identify transactions associated with multiple parties and occurring during a time and/or at a location of the event. At a conclusion of the event, the transactions may be aggregated and an optimal payment scheme may be determined for settlement of the transactions between the parties. In accordance with the determined payment scheme, delegation of portions of the aggregated transactions may be initiated for settlement amongst the parties.

Abstract: Methods, systems, devices, and computer-readable media for detecting multi-party events and transactions are provided. User data may be monitored to detect data associated with an event involving multiple individuals, such as by identifying transaction data associated with certain types of merchants and/or scheduling, calendar, or correspondence data indicative of an event. The data may be further analyzed to identify a date, location, and/or parties associated with the event. A multi-party event may be generated. The user data may continue to be monitored to identify transactions associated with multiple parties and occurring during a time and/or at a location of the event. At a conclusion of the event, the transactions may be aggregated and an optimal payment scheme may be determined for settlement of the transactions between the parties. In accordance with the determined payment scheme, delegation of portions of the aggregated transactions may be initiated for settlement amongst the parties.

Abstract: Methods, systems, devices, and computer-readable media for a multi-party transaction decisioning system are provided. The system may analyze a purchase transaction associated with a first user and may identify that the transaction is associated with more than one party based on determining that an amount of the transaction is atypical for the first user. The system may identify a party associated with the multi-party transaction based on analyzing data associated with the first user. The system may generate a delegation request for a portion of the amount of the multi-party transaction to be delegated to the identified party and may transmit the delegation request to the identified party. Subsequently, the system may receive a payment transaction from the identified party, and the system may identify that the payment transaction is associated with the previously transmitted delegation request.

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 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.