Visa International Service Association

Shi Cao

Chiranjeet Chetia

Xiao Tian

Claudia Carolina Barcenas Cardenas

Jianhua Huang

Marc Corbalan Vila

Wei Zhang

Hao Yang

Liang Wang

Minghua Xu

Junpeng Wang

Dan Wang

Shubham Agrawal

Harishkumar Sundarji Majithiya

Roshni Ann Samuel

Xi Kan

David Stoddard Lambertson

Beatrice-Atena Faurescu

Provided are a method, system, and computer program product for generating synthetic data. The method includes generating a correlation graph of a plurality of data types based on a plurality of correlations. The method also includes generating a directed acyclic graph of the plurality of data types based on the correlation graph. The method further includes traversing the directed acyclic graph to produce a hierarchical graph of the plurality of data types, wherein the hierarchical graph includes a plurality of nodes, and wherein each node of the plurality of nodes is associated with a data type of the plurality of data types. The method further includes generating synthetic training data including a plurality of records of data by repeatedly traversing the hierarchical graph and based on a plurality of sets of values and a plurality of sets of interdependencies.

System, method, and computer program product for generating synthetic data

Methods, systems, and computer program products for learning continuous embedding space of real time payment (RTP) transactions are provided. A method may include receiving RTP data including a plurality of attributes, including a sender and a receiver. One attribute is selected as a target attribute. The remaining attributes are input into a first machine learning model (e.g., NLP model), comprising at least one embedding layer and one hidden layer, which is trained to predict the target attribute. After the model is trained, each of the remaining attributes are converted to a first vector using the at least one embedding layer of the machine learning model to form a first set of vectors. The first set of vectors are stored and subsequently input into a second machine learning model to perform at least one second task different than the first task.

System, method, and computer program product for learning continuous embedding space of real time payment transactions

Provided is a method for detecting group activities in a network. The method may include receiving interaction data associated with a plurality of interactions. For each account identifier associated with at least one interaction, a value may be determined for each of a first set of categories, and a vector may be generated based on the value for each category. The length of each vector may be determined. At least one relational graph may be generated based on the interaction data. Each relational graph may be associated with a respective category of a second set of categories. At least one cluster of nodes may be determined based on the relational graph(s). A score for each cluster may be determined based on the length of the vector associated with the account identifier of each node of the cluster of nodes. A system and computer program product are also disclosed.

Method, system, and computer program product for detecting group activities in a network

Described are a system, method, and computer program product for breach detection using convolutional neural networks. The method includes receiving transaction data associated with a plurality of transactions completed in a first time period. The method also includes identifying a set of suspected fraudulent transactions of the plurality of transactions based on inputting at least one parameter of the transaction data into a fraud evaluation model. The method further includes generating an image comprising a field of points, wherein each point is associated with at least one transaction of the set of suspected fraudulent transactions, and wherein an x-axis position in the image of each point in the field of points is associated with a time subperiod of the first time period in which the at least one transaction occurred. The method further includes detecting a breach event by processing the image with a convolutional neural network (CNN) model.

System, method, and computer program product for breach detection using convolutional neural networks

A computer-implemented method includes: receiving an inquiry request message identifying a first payment transaction having a plurality of transaction parameters and a risk score, where the risk score is generated by a machine-learning model based on the plurality of transaction parameters; for each transaction parameter of the plurality of transaction parameters, perturbing a value of the transaction parameter and re-analyzing the first payment transaction with the machine-learning model to generate a perturbed risk score based on the perturbed transaction parameter; determining at least one impact parameter from the plurality of transaction parameters by comparing the perturbed risk scores generated for each of the plurality of transaction parameters; and generating an inquiry response message based on the at least one impact parameter.

System, method, and computer program product for interpreting black box models by perturbing transaction parameters

Provided are a system, method, and computer program product for generating synthetic data. The method includes receiving a plurality of data types associated with an environment to be evaluated and receiving a plurality of correlations of one data type to another data type. The method also includes generating a correlation graph of the plurality of data types based on the plurality of correlations and generating a directed acyclic graph of the plurality of data types based on the correlation graph. The method further includes generating a hierarchical graph of the plurality of data types by applying a path traversal technique to the directed acyclic graph and generating a synthetic dataset by repeatedly traversing the hierarchical graph to generate a plurality of records of data.

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Austin, TX, United States of America

Shi Cao