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, methods, and apparatuses for blocking subscription transactions for a given subscription from being applied to an electronic payment method, while allowing other transactions to be applied to the electronic payment method. Aspects further comprise training a machine learning model, based on historical transaction data, to predict subscription transactions, and updating the machine learning model based on incoming transactions. Aspects further provide for allowing a user to indicate they wish to cancel a subscription, blocking charges to the subscription while communicating with the merchant to cancel the subscription, and then removing the block after the subscription is confirmed canceled. to Aspects further provide for detecting when a subscription transaction was not blocked properly and updating the machine learning model to block similar subscription transactions in the future.

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: Aspects provided may allow for a user to block charges from an entity without having to contact the entity. After detecting that the user has enrolled in a trial or subscription of an entity offering, the user may be provided with the option to block charges from the entity. Using incoming data, charges from the blocked entity may be identified and prevented from being applied to the user. Further aspects may provide for more discrete charge blocking, such as blocking charges for one activity while allowing other charges for different activities from the same entity to go through, and training an identification model that isolates the entity identifier from the incoming data and matches the entity identifier to known entities.

Abstract: Aspects provided may allow for a user to block charges from an entity without having to contact the entity. After detecting that the user has enrolled in a trial or subscription of an entity offering, the user may be provided with the option to block charges from the entity. Using incoming data, charges from the blocked entity may be identified and prevented from being applied to the user. Further aspects may provide for more discrete charge blocking, such as blocking charges for one activity while allowing other charges for different activities from the same entity to go through, and training an identification model that isolates the entity identifier from the incoming data and matches the entity identifier to known entities.

Abstract: A system may identify, using a machine learning model, a series of recurring events associated with an account and may generate, using the machine learning model, a prediction of a future date on which a predicted event, associated with the series of recurring events, is to occur. The system may determine that a condition associated with the account is satisfied and may determine that a current date is within a threshold number of days of the future date based on the prediction of the future date. The system may transmit, to a user device, a notification based on determining that the current date is within the threshold number of days of the future date and that the condition associated with the account is satisfied, wherein the notification includes information for presentation of an input element that enables an action to be performed in connection with the account.

Abstract: A system may identify, using a machine learning model, a series of recurring events associated with an account and may generate, using the machine learning model, a prediction of a future date on which a predicted event, associated with the series of recurring events, is to occur. The system may determine that a condition associated with the account is satisfied and may determine that a current date is within a threshold number of days of the future date based on the prediction of the future date. The system may transmit, to a user device, a notification based on determining that the current date is within the threshold number of days of the future date and that the condition associated with the account is satisfied, wherein the notification includes information for presentation of an input element that enables an action to be performed in connection with the account.

Abstract: Systems, methods, and apparatuses for determining, based on historical transaction data, that past charges for a merchant service to an electronic payment method of a user correspond to a subscription type recurring transaction, and providing a user with options to manage the service are described. Based on a machine learning model analyzing the historical transaction data comprising past charges for a service of a corresponding merchant of the plurality of merchants, generating a probability that the past charges for the service indicate a subscription type recurring transaction, for the service of the corresponding merchant. Based on the probability exceeding a threshold, determining that the past charges correspond to a subscription type recurring transaction, an expected amount of an upcoming charge and an expected payment date for the upcoming charge. Based on the determining, providing one or more options for a user to alter the subscription type service.

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: A system and method for detecting a recurring charge that is unknown to the customer and/or the result of fraud or deceit is disclosed. A customer complaint initiates an analysis of a particular transaction. In response to the complaint, information relating to the transaction is extracted and used to identify additional information from a variety of different sources. Such information may include information relating to the customer, information relating to the merchant, as well as information relating to past transactions or related transactions. This information is provided to a machine learning algorithm, both for training purposes and for analysis purposes. The machine learning algorithm analyzes a transaction in order to determine the likelihood that the transaction is a recurring transaction, as well as the likelihood that it is a result of a bad actor, fraud or deceit.

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: Aspects provided may allow for a user to block charges from an entity without having to contact the entity. After detecting that the user has enrolled in a trial or subscription of an entity offering, the user may be provided with the option to block charges from the entity. Using incoming data, charges from the blocked entity may be identified and prevented from being applied to the user. Further aspects may provide for more discrete charge blocking, such as blocking charges for one activity while allowing other charges for different activities from the same entity to go through, and training an identification model that isolates the entity identifier from the incoming data and matches the entity identifier to known entities.

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: A system may identify, using a machine learning model, a series of recurring events associated with an account and may generate, using the machine learning model, a prediction of a future date on which a predicted event, associated with the series of recurring events, is to occur. The system may determine that a condition associated with the account is satisfied and may determine that a current date is within a threshold number of days of the future date based on the prediction of the future date. The system may transmit, to a user device, a notification based on determining that the current date is within the threshold number of days of the future date and that the condition associated with the account is satisfied, wherein the notification includes information for presentation of an input element that enables an action to be performed in connection with the account.

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.