Abstract: Systems and methods that may be used to generate and use a social graph generated by user financial transaction data (i.e., a financial transaction-based social graph). Connections and other data within the financial transaction-based social graph can be used for targeted product offerings, other offerings, and or advertisements via e.g., collaborative filtering and user segmentation and profiling.

Abstract: Systems and methods that may be used to generate and use a social graph generated by user financial transaction data (i.e., a financial transaction-based social graph). Connections and other data within the financial transaction-based social graph can be used for targeted product offerings, other offerings, and or advertisements via e.g., collaborative filtering and user segmentation and profiling.

Abstract: A method including establishing, using electronic transactions of a user, a geo-temporal trajectory of the user. The method also includes forming a first data structure by sub-dividing the geo-temporal trajectory into segments including subsets of the electronic transactions along the geo-temporal trajectory. Sub-dividing is performed with respect to a selected feature. The method also includes gathering, for a subset of the segments, a corresponding labeled dataset of transactions within the electronic transactions to generate a second data structure. The method also includes applying, as input, the second data structure to a machine learning classifier. The method also includes receiving, from the machine learning classifier, an assignment of disambiguated labels to the electronic transactions. The method also includes storing, automatically in a financial management application, the disambiguated labels as assigned to the electronic transactions.

Abstract: A machine learning method. A source domain data structure and a target domain data structure are combined into a unified data structure. First data in the source domain data structure are latent with respect to second data in the target domain data structure. The unified data structure includes user vectors that combine the first data and the second data. The user vectors are transformed into a transformed data structure by applying a mapping function to the user vectors. The mapping function relates, using at least one parameter, first relationships in the source domain data structure to second relationships in the target domain data structure. The at least one parameter is based on a combination of affinity scores relating items with which the user interacted and did not interact. The transformed data structure is input into a machine learning model, from which is obtained a recommendation relating to the target domain.

Abstract: Systems and methods that may be configured to implement a nearest neighbor-based bank account validation process that may be used with electronic payments, transactions and or services.

Abstract: A processor may generate a plurality of vectors from an original text by processing the original text with at least one unsupervised learning algorithm. Each of the plurality of vectors may correspond to a separate portion of a plurality of portions of the original text. The processor may determine respective segments to which respective vectors belong. The processor may minimize a distance between at least one vector belonging to the segment and a known vector from among one or more known vectors and applying a label of the known vector to the segment.

Abstract: A computer-implemented method is provided to preforming re-categorization of financial transactions. The re-categorization is implemented by a server computing device which receives the financial transactions associated with a merchant and a first category. The server computing device receives user inputs that are each associated with re-categorizing a financial transaction from the first category to one or more other categories. Based at least in part on a count of the first category and counts of the one or more other categories, the server computing device determines a set of normalized ratios for the first category and the one or more other categories with respect to a total number of respective financial transactions received. The server computing device determines a second category corresponding to a minimum value in the set of the normalized ratios for each financial transaction associated with the merchant.

Abstract: One or more embodiments includes storing a group email message in multiple email mailboxes to obtain multiple stored email messages, receiving, for a first stored email message in the stored email messages, an updated label from a first user computing device, detecting, in response to receiving the updated label, that the stored email messages matches the first stored email message, and storing the updated label with at least a subset of the stored email messages. The method further includes transmitting, with the updated label and to a second user computing system, a second stored email message in the stored email messages.

Abstract: Systems and methods that approximate and use branching operations on data encrypted by fully homomorphic encryption (FHE). The systems and methods may use polynomial approximation to convert “if” statements into “soft if” statements that may be applied to the FHE encrypted data in a manner that preserves the security of the systems and methods.

Abstract: A method including receiving a data structure describing transactions between electronic user accounts associated with users. A relationship graph is constructed from the data in the data structure. The relationship graph has nodes representing entities described in the transactions. The relationship graph has edges representing connections between the nodes. The method also includes clustering groups of nodes within the nodes to form clusters among the nodes. The edges are labeled as relationships types. Labeling is performed by receiving, as input to a machine learning model, a vector having attributes representing the clusters, the nodes, and the edges. Labeling is also performed by outputting, from the machine learning model, probabilities. Each of the probabilities corresponds to a corresponding probability that an edge in the edges represents a relationship type between two nodes in the nodes. Labeling is also performed by labeling, based on the output, the edges as the relationship types.

Abstract: Dominant sequences that are representative of logged events can be artificially created. Initially, a graph comprising multiple nodes and edges between pairs of nodes is generated from logged information. The weights, or values, associated with edges are incremented as the log data reveals a temporal relationship between two nodes. Subsequently, a set of candidate trajectories, with each candidate trajectory representing a sequence of events, are generated by repeatedly traversing the generated graph in a random manner by commencing at randomly selected nodes and then proceeding in a random manner to subsequent nodes in accordance with the edge values, for a random quantity of steps. The candidate trajectories are filtered to eliminate those that are impossible or improbable based on a comparison between individual candidate trajectories and the quantity of occurrences within the logs. Scoring is based both on a quantity of occurrences as well as a quantity of steps.

Abstract: Dominant sequences that are representative of logged events can be artificially created. Initially, a graph comprising multiple nodes and edges between pairs of nodes is generated from logged information. The weights, or values, associated with edges are incremented as the log data reveals a temporal relationship between two nodes. Subsequently, a set of candidate trajectories, with each candidate trajectory representing a sequence of events, are generated by repeatedly traversing the generated graph in a random manner by commencing at randomly selected nodes and then proceeding in a random manner to subsequent nodes in accordance with the edge values, for a random quantity of steps. The candidate trajectories are filtered to eliminate those that are impossible or improbable based on a comparison between individual candidate trajectories and the quantity of occurrences within the logs. Scoring is based both on a quantity of occurrences as well as a quantity of steps.

Abstract: Dominant sequences that are representative of logged events can be artificially created. Initially, a graph comprising multiple nodes and edges between pairs of nodes is generated from logged information. The weights, or values, associated with edges are incremented as the log data reveals a temporal relationship between two nodes. Subsequently, a set of candidate trajectories, with each candidate trajectory representing a sequence of events, are generated by repeatedly traversing the generated graph in a random manner by commencing at randomly selected nodes and then proceeding in a random manner to subsequent nodes in accordance with the edge values, for a random quantity of steps. The candidate trajectories are filtered to eliminate those that are impossible or improbable based on a comparison between individual candidate trajectories and the quantity of occurrences within the logs. Scoring is based both on a quantity of occurrences as well as a quantity of steps.