Abstract: There are provided systems and methods for location-based data tracking for dynamic data presentation on mobile devices. A user's device may be used to track user data for the user, including locations visited by the user and activities by the user at the locations. These may be correlated to likely behavior by the user at the location so that a predicted activity by the user at a location may be determined. Thus, when a user visits a location, the predicted activity of the user at the location may be determined. Using the predicted activity, application data for an application may be generated and may be dynamically presented through one or more interfaces of the user's device. This may be presented without user input at the location so that the user may quickly perform the activity through the user's device.

Abstract: Methods and systems are presented for performing comprehensive and accurate matching of user accounts with one or more known entities based on image-linking graphs. Images related to each known entity are retrieved from one or more online sources. Faces are extracted from the images. Based on attributes of the faces in the images, an image-linking graph is generated for the entity. When a user account is determined to be a potential match for the entity based on text-based attributes, an image associated with the account may be obtained. If the image matches with any one of the faces in the image-linking graph, an action is performed to the user account based on a position of the matched face in the image-linking graph.

Abstract: There are provided systems and methods for a machine learning model and narrative generator for prohibited transaction detection and compliance. A service provider server, such as an electronic transaction processor, may generate a machine learning model using a supervised training technique, which may detect transactions that may be money laundering. The model may be iteratively trained by detecting flagged transactions and outputting those transactions to an agent for identification of false positives, which may be used to retrain the model. When outputting the flagged transactions, a narrative may be generated using an explainer graph and a machine learning prediction explainer that identifies the features of the transaction data that caused the transactions to be flagged. Further, once the model is trained additional transactions may be processed to determine whether the features of those transactions indicate prohibited behavior.

Abstract: A plurality of first entities have been previously associated with a predefined activity. By performing a clustering algorithm on the first entities, a subset of the first entities is identified that have met a predefined criterion. Via a Natural Language Processing (NLP) technique, a multi-dimensional matrix is generated. The matrix has a plurality of vectors associated with attributes of the subset of the first entities. A neural network model is trained with the multi-dimensional matrix. A plurality of second entities are on a list that contains entities that have been flagged for engaging in, or having engaged, the predefined activity. Based on the trained neural network model, a prediction is made whether scanning the second entities against a plurality of third entities for matches will cause a number of alerts having a predefined characteristic to exceed a predefined threshold. The alerts correspond to matches that needs further investigation.

Abstract: There are provided systems and methods for a machine learning model and narrative generator for prohibited transaction detection and compliance. A service provider server, such as an electronic transaction processor, may generate a machine learning model using a supervised training technique, which may detect transactions that may be money laundering. The model may be iteratively trained by detecting flagged transactions and outputting those transactions to an agent for identification of false positives, which may be used to retrain the model. When outputting the flagged transactions, a narrative may be generated using an explainer graph and a machine learning prediction explainer that identifies the features of the transaction data that caused the transactions to be flagged. Further, once the model is trained additional transactions may be processed to determine whether the features of those transactions indicate prohibited behavior.

Abstract: Systems and methods that determining a solution for a real-time message are provided. Multiple messages of different types are received from multiple platforms. The messages were generated in response to errors caused by applications monitored by the platforms. For each message, a language processing system determines the content of the message and the machine learning system determines a classification of the message. The set of message candidates are generated by comparing the classification and the content of the message to historical messages. From the set of message candidates, solution messages are identified. A recommended solution is determined from the solution messages.

Abstract: There are provided systems and methods for location-based data tracking for dynamic data presentation on mobile devices. A user's device may be used to track user data for the user, including locations visited by the user and activities by the user at the locations. These may be correlated to likely behavior by the user at the location so that a predicted activity by the user at a location may be determined. Thus, when a user visits a location, the predicted activity of the user at the location may be determined. Using the predicted activity, application data for an application may be generated and may be dynamically presented through one or more interfaces of the user's device. This may be presented without user input at the location so that the user may quickly perform the activity through the user's device.

Abstract: There are provided systems and methods for location-based data tracking for dynamic data presentation on mobile devices. A user's device may be used to track user data for the user, including locations visited by the user and activities by the user at the locations. These may be correlated to likely behavior by the user at the location so that a predicted activity by the user at a location may be determined. Thus, when a user visits a location, the predicted activity of the user at the location may be determined. Using the predicted activity, application data for an application may be generated and may be dynamically presented through one or more interfaces of the user's device. This may be presented without user input at the location so that the user may quickly perform the activity through the user's device.

Abstract: There are provided systems and methods for a machine learning model and narrative generator for prohibited transaction detection and compliance. A service provider server, such as an electronic transaction processor, may generate a machine learning model using a supervised training technique, which may detect transactions that may be money laundering. The model may be iteratively trained by detecting flagged transactions and outputting those transactions to an agent for identification of false positives, which may be used to retrain the model. When outputting the flagged transactions, a narrative may be generated using an explainer graph and a machine learning prediction explainer that identifies the features of the transaction data that caused the transactions to be flagged. Further, once the model is trained additional transactions may be processed to determine whether the features of those transactions indicate prohibited behavior.

Abstract: Systems and methods that determining a solution for a real-time message are provided. Multiple messages of different types are received from multiple platforms. The messages were generated in response to errors caused by applications monitored by the platforms. For each message, a language processing system determines the content of the message and the machine learning system determines a classification of the message. The set of message candidates are generated by comparing the classification and the content of the message to historical messages. From the set of message candidates, solution messages are identified. A recommended solution is determined from the solution messages.

Abstract: There are provided systems and methods for location-based data tracking for dynamic data presentation on mobile devices. A user's device may be used to track user data for the user, including locations visited by the user and activities by the user at the locations. These may be correlated to likely behavior by the user at the location so that a predicted activity by the user at a location may be determined. Thus, when a user visits a location, the predicted activity of the user at the location may be determined. Using the predicted activity, application data for an application may be generated and may be dynamically presented through one or more interfaces of the user's device. This may be presented without user input at the location so that the user may quickly perform the activity through the user's device.

Abstract: Methods and systems are presented for reducing false positives in detecting profiles that are connected to an entity within a list of entities. A set of profiles may be matched with the entity based on information associated with the entity. The information associated with the entity may be enriched based on common attributes that are shared among the entities within the list. A machine learning model may be used to determine a likelihood that a matched profile is connected to the entity based on the enriched information. Profiles having corresponding likelihoods below a predetermined threshold may be removed from the set of matched profiles. The matched profiles may be clustered around the entity based on a set of attributes derived from the enriched information, and profiles that fall outside of the cluster may be further removed.