Abstract: Aspects of the present disclosure involve systems, methods, devices, and the like for tokenizing offers. The current disclosure presents a system and method that can present a tokenized offer for saving and retrieving from a digital wallet. The tokenized offer may be presented on a client site for selection by a user interested in taking advantage of the promotion without having to leave the current site. The current disclosure also presents a system that can retrieve the tokenized offer saved for use with a purchase. The tokenized offer may be automatically applied and on display at the merchant site and/or retrieved from the digital wallet.

Abstract: There are provided systems and methods for load balancing for map application route selection and output. A user may utilize a device application to map or route between two or more endpoints, such as geo-locations entered or detected by the device. During calculation of a travel route between the endpoints, real-time data, user preferences, and requesting entities may provide criteria data that may cause determination of a particular travel route, where the travel route may be longer than a most efficient route but within a pre-defined variable time or distance allotment and match the criteria data. Use of the route may accrue a form of compensation for the user. The user may view an application interface displaying the route, which may further include one or more executable processes to cause recalculation of the route. Recalculation of the route may require the user to provide credits or compensation.

Abstract: Systems and methods are disclosed to provide an animated 2D barcode check that is used to securely and efficiently transmit financial information between mobile devices. The financial information includes a check written by a payer and transmitted to a merchant. The check includes account information of the payer and of the merchant with a payment service provider, a payment amount, and a cryptographic signature that has been certified by the payment service provider. The payer's mobile device encodes the check into a sequence of 2D barcodes and displays the sequence of 2D barcodes in a loop that is scanned by a camera on the merchant's mobile device. The merchant uses the cryptographic signature to verify the validity of the signature on the check without having Internet connectivity to the payment service provider. The merchant may later present the check to the payment service provider to receive payment.

Abstract: Systems and methods for user-agent anomaly detection are disclosed. In one embodiment, a user-agent string may be embedded into a numerical data vector representation using a sentence embedding algorithm (e.g., FastText). A predictive score may be calculated based on the numerical data vector representation and using a probability distribution function model that models a likelihood of occurrence of the observed user-agent based on patterns learned from historic payload data (e.g., a Gaussian Mixture Model). The predictive score may be compared to a threshold and, based on the comparison, it may be determined whether the user-agent is fraudulent.

Abstract: Pure machine learning classification approaches can result in a “black box” solution where it is impossible to understand why a classifier reached a decision. This disclosure describes generating new classification rules leveraging machine learning techniques. New rules may have to meet evaluation criteria. Legibility of those rules can be improved for understanding. A machine learning classifier can be created that is used to identify possible candidate classification rules (e.g. from a group of decision trees such as a random forest classifier). Classification rules generated with the assistance of machine learning may allow for identification of transaction fraud or other classifications that a human analyst would be unable to identify. A selection process can identify which possible candidate rules are effective. The legibility of those rules can then be improved so that they can be more easily understood by humans.

Abstract: Methods and systems are presented for enhancing a merchant product webpage by providing additional content associated with a product being displayed on the merchant product webpage. A product catalog stores product records corresponding to data associated with different products. As a webpage is accessed by a user device, data is extracted from the merchant product webpage. The extracted data is used to identify one or more product records in the product catalog that are potential matches to the webpage. When multiple product records are identified, a remote server is used to access and interact with the webpage separate from the user device. Additional data is extracted from the webpage based on interactions by the remote server with the webpage. The additional data is used to determine a single product record for the webpage. The webpage is modified based on the single product record.

Abstract: A risk analysis system configures the decision engine to detect anomalous online activities by analyzing usage patterns associated with one or more user accounts across multiple frequencies. The risk analysis system obtains transaction log data representing transactions associated with one or more accounts, and extracts data from the transaction log data to generate time-series data along a time dimension. The time-series data may represent usage characteristics of one or more user accounts over a period of time. The risk analysis system derives pattern data representing usage patterns across multiple different frequencies based on the time-series data. The risk analysis system then configures the decision engine to detect anomalous account activities based on the derived pattern data.

Abstract: Methods and systems are presented for providing a multi-party computation (MPC) framework for dynamically configuring, deploying, and utilizing an MPC system for performing distributed computations. Based on device attributes and network attributes associated with computer nodes that are available to be part of the MPC system, a configuration for the MPC system is determined. The configuration may specify a total number of computer nodes within the MPC system, a minimum number of computer nodes required to participate in performing a computation process, a key distribution mechanism, and a computation processing mechanism. Encryption keys are generated and distributed among the computer nodes based on the key distribution mechanism. Upon receiving a request for performing the computation, updated network attributes are obtained.

Abstract: A plurality of user segments is defined. Each user segment has a respective profile corresponding to one or more characteristics shared by one or more users. A plurality of elements of a user interface for a mobile application is defined. Each user segment is associated with a different subset of the elements. A first request is received to display the user interface on a first mobile device of a first user. In response to the first request, user data of the first user is analyzed. Based on the analysis, a first user segment to which the first user belongs is determined. The mobile application is then instructed to display the user interface according to a first customized layout on the first mobile device. The first customized layout includes a first subset of elements associated with the first user segment.

Abstract: Techniques are disclosed in which a computer system generates a transaction network graph from an initial set of transactions including known labels and attributes. The computer system may generate first and second matrices using first and second graph embedding routines from a training set of transactions that includes a first subset of transactions in the network graph. The first routine is based on anomalies in related transactions occurring at nodes in the transaction network graph that are multiple hops away while the second routine is based on anomalies in neighborhoods of similar transactions. In some embodiments, the computer system generates a final embedded matrix from the first and second matrices and uses the final matrix and a testing set of transactions that includes a second subset of transactions in the graph to train a machine learning model, where the trained model usable to determine whether unlabeled transactions are anomalous.

Abstract: There are provided systems and methods for edge computing storage nodes based on location and activities for user data separate from cloud computing environments. A service provider, such as an online transaction processor, may provide additional services for to users via edge computing systems and edge computing storage nodes. The service may be for data that may be predictively loaded to the edge computing storage node for a particular location, where the edge computing storage node may reside more locally to the location on a network so that data may be served quicker and with less network resource consumption than providing data from a remote cloud computing storage. The data may be predicted to be needed or useful to the user at the location using a user profile for the user, monitored user activities, and/or one or more machine learning models that predict user behaviors at the location.

Abstract: The systems and methods that generate a one-touch frame are provided. A line of code including a tag is inserted into a webpage. The tag includes a link to a server from which parameters for the frame are obtained. The browser uses the parameters to generate the frame. In addition, the server or a local memory of a computing device on which the frame is generated also store credentials associated with a user account. These credentials are also obtained and incorporated into the frame. The frame may then be activated to perform one-touch functions using the credentials.

Abstract: A method may include determining that input text data includes a first keyword from a first set of keywords. The method also includes determining a similarity between the input text data and a first stored text string that has previously been identified as a false positive match for the first keyword, and based on the similarity, generating a first false positive score corresponding to the input text data. Further, the method includes determining a number of keywords, from a second set of keywords, that are included in the input text data, and based on the number of keywords, generating a second false positive score corresponding to the input text data. The method also includes calculating a final false positive score corresponding to the input text data based on the first false positive score and the second false positive score.

Abstract: Techniques for managing erasure of data are presented. In response to receiving a request for erasure of data from a set of data stores, an erasure component can analyze a set of rules and information relating to the user account, including an account status and erasure hold status associated with the user account. The set of rules can be based on legal or contractual obligations applicable to the set of data stores, and can indicate various conditions under which data associated with a user account of a user can be eligible to be erased from the set of data stores or an associated data vault repository. The erasure component can determine eligibility for erasure of all or a portion of the set of data from the set of data stores based on the analysis results. Erasure component can determine erasure eligibility scores to pre-qualify user accounts for erasure eligibility.

Abstract: Methods and systems for providing merchant in-context checkout are described. A user is authenticated based on credentials received from a first application running on a computing device. An authentication code is provided to the first application. A signed verifier and the authentication code is then received from a second application running on the computing device. The authentication code and the signed verifier received from the second application are then validated, and a device token is provided to the second application upon validation. The device token is exchangeable by the second application for an access token that is usable for making payment calls from the second application.

Abstract: Methods and systems are presented for detecting groups within a graph using computer-based multi-threading techniques. These techniques provide technical improvements in computing power and efficiency for analysis of large graphs. A group detection system accesses a graph. Threads are instantiated to perform task related to a group detection process based on the nodes in the graph, where a thread is instantiated for a corresponding node. Each thread determines a neighbor count representing a number of neighbor nodes having one degree of separation from the corresponding node. Each thread also generates a list comprising an identity of the corresponding node and identities of the neighbor nodes. The thread transmits the list only to threads corresponding to a first subset of the neighbor nodes having more neighbors than the corresponding node, but not to threads corresponding to a second subset of the neighbor nodes having less neighbors than the corresponding nodes.

Abstract: Methods and systems for using machine learning to automatically determine a data loading configuration for a computer-based rule engine are presented. The computer-based rule engine is configured to use rules to evaluate incoming transaction requests. Data of various data types may be required by the rule engine when evaluating the incoming transaction requests. The data loading configuration specifies pre-loading data associated with at least a first data type and lazy-loading data associated with at least a second data type. Statistical data such as use rates and loading times associated with the various data types may be supplied to a machine learning module to determine a particular loading configuration for the various data types. The computer-based rule engine then loads data according to the data loading configuration when evaluating a subsequent transaction request.

Abstract: Computer system security and efficiency of processing operations may be improved using techniques that are described relating to analyzing user actions based on time of day of occurrence, and using time of a day as a factor in determining whether a particular action should be allowed or disallowed. Past action times can be transformed to a two-dimensional representation using a radial time schema that avoids discontinuity. A probability distribution can indicate a likelihood of whether a new action fits a previous pattern. If a new user action is relatively unlikely due to time of day, the new user action might be denied/prevented from completing, thus enhancing computer system security and avoiding unnecessary computational processing costs.

Abstract: Systems and methods for recommending merchant actions include a physical merchant location having a plurality of beacon devices. A system provider device receives a plurality of first location information from the plurality of beacon devices that is collected from a customer beacon communication device that is associated with a customer. The system provider device also receives a plurality of second location information over the network from the plurality of beacon devices that is collected from a merchant beacon communication device that is associated with an asset of the merchant. The system provider device analyzes the plurality of first location information and the plurality of second location information to determine at least one merchant action recommendation related to the customer and the asset of the merchant and provides the at least one merchant action recommendation over the network for display on a display device.

Abstract: A method includes receiving a set of training data and selecting a first machine learning platform based on a first optimization function that metrics past machine learning platforms used for training on the set of training data. The method also includes selecting a first algorithm supported by the first machine learning platform based on a second optimization function that metrics past algorithms used for training on the set of training data. Further, the method includes determining one or more hyperparameters supported by the first algorithm based on a third optimization function that metrics past combinations of hyperparameters from the set of hyperparameters used for training on the set of training data. The method also includes training a machine learning model on the set of training data using the first machine learning platform, the first algorithm, and the one or more hyperparameters.