Abstract: Knowledge about a user is used to determine whether one or more messages received by the user are malicious. The knowledge about the user may be based on the user's financial history such as transaction records. Particularly, a classifier model is trained on a supervised approach using a dataset containing, for example, a categorization of incoming messages (e.g., password change message), the user's aggregated transaction records, message attributes, user attributes, and corresponding classification labels. After the training, the classifier model is deployed to determine whether an incoming message is malicious.

Abstract: A processor may receive an image and determine a number of foreground pixels in the image. The processor may obtain a result of optical character recognition (OCR) processing performed on the image. The processor may identify at least one bounding box surrounding at least one portion of text in the result and overlay the at least one bounding box on the image to form a masked image. The processor may determine a number of foreground pixels in the masked image and a decrease in the number of foreground pixels in the masked image relative to the number of foreground pixels in the image. Based on the decrease, the processor may modify an aspect of the OCR processing for subsequent image processing.

Abstract: A method implements churn prevention using graphs. The method includes receiving clickstream data, which includes an event, of a user session with an application. The method further includes identifying the event as corresponding to a churn user account and mapping the event to a pair of nodes of a graph. The method further includes updating a churn user count of the pair of nodes in response to identifying the event as corresponding to the churn user account. The method further includes identifying an edge of the graph, corresponding to the pair of nodes. The method further includes updating a value of the edge using an active user count and the churn user count presenting an update responsive to the value.

Abstract: A method implements churn prediction using clickstream data. The method includes receiving clickstream data of a user and converting the clickstream data to a token list. The method further includes processing the token list with a first recurrent layer, a second recurrent layer, and an attention layer of a machine learning model to generate a churn risk. The method further includes executing a reactivation action in response to the churn risk.

Abstract: A method includes extracting attribute values of attributes from login events, filtering the attribute values based on correlation between the attributes and classes to obtain filtered attributes values, and generating a vector embedding of the filtered attributes values to obtain login vectors. The method further includes executing a sequential machine learning model on the login vectors to determine a class of the classes, and outputting the class.

Abstract: Certain aspects of the present disclosure provide techniques for generating a recommendation of third-party applications to a user by a recommendation engine. The recommendation engine includes two deep-learning models that use various data sources (e.g., user data and application data) to generate the recommendation. One deep-learning model generates a relevance score for each available third-party application. The relevance score is used to determine a relevant application(s). The other deep-learning model generates a connection score for each relevant application. The recommendation engine uses the relevance score and the connections to generate an engagement score for each relevant application to determine whether the user would use the third-party application if recommended to the user. Those relevant applications with an engagement score that meet pre-determined criteria are determined and displayed to the user in the application as a recommendation.

Abstract: A computer-implemented system and method for generating and implementing real-time optimized savings recommendations during online purchase checkout processes. The recommendations may be in the form of personalized digital nudges designed to influence the user in a manner that furthers a savings goal.

Abstract: In one aspect, the present disclosure relates to a method of generating business descriptions performed by a server, said method may include: receiving a plurality of invoices, each invoice being associated with a business of a plurality of businesses; extracting a plurality of texts from the plurality of invoices; embedding the plurality of texts to a vector space to obtain a plurality of invoice vectors; generating a plurality of clusters in the vector space, each cluster of the plurality of clusters comprising at least one invoice vector of the plurality of invoice vectors; generating a description for a cluster, the description for the cluster representing all invoice vectors assigned to the cluster; for each business of the plurality of businesses that has at least one invoice vector assigned to the cluster, associating the business with the description; and indexing the plurality of businesses within a database by the generated descriptions.

Abstract: A method identifying clusters with anomaly detection. The method includes aggregating a set of events, of a user, to generate a user vector in response to identifying an event of the set of events. The method further includes aggregating a set of user vectors to a periodic vector for a time period. The method further includes processing a set of periodic vectors to generate a periodic distance. The method further includes selecting the time period, corresponding to the periodic vector, using the periodic distance and a threshold. The method further includes processing the set of user vectors to generate clusters of user vectors, wherein the set of user vectors includes the event during the time period. The method further includes processing the clusters of user vectors to identify a selected cluster and performing an action to a set of user accounts corresponding to the selected cluster.

Abstract: Aspects of the present disclosure provide techniques for categorical anomaly detection. Embodiments include receiving values for a plurality of data categories for an entity of a plurality of entities. Embodiments include generating a feature vector for the entity based on the values, the feature vector excluding a first value for a first data category of the plurality of data categories. Embodiments include providing one or more inputs to a machine learning model based on the feature vector and determining, based on one or more outputs received from the machine learning model, one or more other entities of the plurality of entities that are grouped with the entity. Embodiments include determining that the first value is anomalous based on respective values for the first data category for the one or more other entities. Embodiments include performing one or more actions based on the determining that the first value is anomalous.

Abstract: Systems and methods are disclosed. An example method may be performed by one or more processors of a system and include retrieving case data indicating, for each respective case of a number of cases, one or more documents retrieved to assist a system user associated with the respective case, generating, from the case data, a case matrix including a plurality of rows each corresponding to a respective case of the number of cases and a plurality of columns each corresponding to the documents retrieved to assist the system user associated with the respective case, and identifying groups of similar cases among the plurality of cases based on a clustering process performed on at least a portion of the case matrix.

Abstract: A method and system provide estimated tax refund data to a user of a tax return preparation system throughout personalized tax return preparation interview. The method and system receive current user tax related data associated with the user, retrieve tax rules data, and gather historical tax related data associated with historical users of the tax return preparation system. The method and system further generate probabilistic inference data including inferences about tax related characteristics of the user based on the historical tax related data and the tax rules data. The method and system provide estimated tax refund data to the user based on the probabilistic inference data.

Abstract: The disclosed system and methods are directed to a data-driven choreography of microservices sagas that enable the composition of microservices without depriving each individual microservice of its independence.

Abstract: Certain aspects of the present disclosure provide techniques for generating a user experience for a software program product based on a knowledge engine. Techniques for generating the user experience include a UI builder tool providing a set of tabular UI views and receiving in each tabular UI view corresponding input data for generating a calculation graph, a completeness graph, and a client UI view. Based on the input data, the UI builder tool and knowledge engine can generate a set of artifact files. The knowledge engine can generate and/or execute the calculation and completeness graphs as defined in the corresponding artifact files. The UI builder tool can generate an instance of the client UI view. With the generated calculation graph(s), completeness graph(s), and an instance of the client UI view, the user experience can be provided to a computing device.

Abstract: Systems and methods of editing a user experience by a what you see is what you get (wysiwyg) editor are disclosed. A system obtains a visible portion of a document object model for a document, with the visible portion including one or more visible objects to be presented to a user during a user experience. Instead of the visible portion being generated by the wysiwyg editor (thus requiring the visible portion to be rendered again at a later time by a rendering engine), the visible portion to be used is generated by the rendering engine based on a schema of the document. To allow such, the system generates and outputs an overlay to be placed on the visible portion during display of the visible portion, with the overlay to receive user inputs to the visible portion and thus prevent user interaction with the visible portion.

Abstract: Aspects of the present disclosure provide techniques for intent matching. Embodiments include receiving input of text by a user via a user interface. Embodiments include determining weights for portions of the text based on a plurality of keywords. Embodiment include generating an embedding of the text. Embodiments include determining an intent of the text by weighting, based on the weights, word mover's distances from the embedding of the text to a known embedding of known text associated with the intent in order to determine a similarity measure between the text and the known text. Embodiments include providing content to the user via the user interface based on the intent.

Abstract: Systems and methods are provided that, in some embodiments that extract user data from at least one data warehouse. The user data is sorted within each dimension, and partitions each dimension into bins. Clusters are defined as each bin that includes user data for a number of users that exceeds a threshold. Clusters are determined for every combination of dimensions. Each combination of clusters that exceed the threshold is defined as clusters that are formed from multiple dimensions. All clusters and other clusters are stored into a cluster definition table. The clusters are used to analyze the profile of specific users.

Abstract: A method includes obtaining, by a computing system executing a declarative code player, a processing widget defining multiple states and executing, by the computing system executing the declarative code player, the processing widget. Executing the processing widget comprises, for each current state of at least a subset of the states obtaining multiple state lifecycle declaration for the current state from a repository, the state lifecycle declaration is specified as declarative content, executing multiple library instructions according to the state lifecycle declaration to perform at least one operation of the current state, and transitioning, as defined by the current state, to a next state as defined in the processing widget.

Abstract: The present disclosure provides techniques for recommending vendors using machine learning models. One example method includes generating a dependency graph based on one or more microservices in a transaction, wherein each node of the dependency graph corresponds to each microservice of the one or more microservices in the transaction, respectively, generating a first set of features using the dependency graph, predicting a first set of nodes that are likely to fail using a first machine learning model based on the first set of features, generating a second set of features using the dependency graph, predicting a second set of nodes that are likely to fail using a second machine learning model based on the second set of features, and applying one or more fixes to one or more microservices based on a combination of the first set of nodes and the second set of nodes.

Abstract: Techniques are disclosed for adjusting a ranking of information content presented to a user based on voice-of-customer feedback. In one embodiment, a user may provide feedback on information content presented to the user. Such feedback may be evaluated to identify at least one topic referenced in the received feedback. If an application determines that the at least one topic is related to topics of the information content, the application determines sentiment regarding the information content based on the feedback, and adjusts a ranking of the information content based on the determined sentiment.