公开(公告)号：US20230325757A1

公开(公告)日：2023-10-12

申请号：US18042968

申请日：2021-07-15

Applicant: MASTERCARD INTERNATIONAL INCORPORATED

Inventor： James Conway ,  Quentin Bragard

IPC: G06Q10/067 ,  G06Q20/20

CPC classification number: G06Q10/067 ,  G06Q20/202

Abstract: A computer-implemented method for testing a model indicating a parametric relationship between a plurality of channels of multivariate data, the method comprising: obtaining a real dataset of observed multivariate data comprising the plurality of channels; generating a control dataset of multivariate data comprising the plurality of channels, the control dataset being generated based on the model; for each of a plurality of sample subsets from the real dataset and the control dataset, calculating a p-value for the model; and determining whether (1) a distribution difference characteristic of a distribution of the obtained p-values for sample subsets of the real dataset and a distribution of the obtained p-values for sample subsets of the control dataset falls within a second predetermined significance range, and, if condition (1) is met, determining that the model is accurate.

公开(公告)号：US20240420147A1

公开(公告)日：2024-12-19

申请号：US18747992

申请日：2024-06-19

Applicant: MASTERCARD INTERNATIONAL INCORPORATED

Inventor： Mohit Taneja ,  Jack Nicholls ,  James Conway ,  Nitish Kothale ,  Shannon Holland ,  Stephen Patrick Flinter ,  Weston Moran

IPC: G06Q20/40 ,  G06Q20/38

Abstract: A computer implemented method of training a model, using a machine learning process, to predict whether a transaction of a digital currency stored in a blockchain is fraudulent, comprises: obtaining (202) transaction data for a first transaction of first funds in the digital currency, wherein the transaction data further comprises information related to a second transaction of the first funds that preceded the first transaction. The method further comprises labelling (204) the transaction data for the first transaction according to whether the first transaction was fraudulent and using (206) the transaction data and the label as training data with which to train the model.

公开(公告)号：US20240420146A1

公开(公告)日：2024-12-19

申请号：US18747982

申请日：2024-06-19

Applicant: MASTERCARD INTERNATIONAL INCORPORATED

Inventor： Mohit Taneja ,  Jack Nicholls ,  James Conway ,  Nitish Kothale ,  Shannon Holland ,  Weston Moran

IPC: G06Q20/40

Abstract: A computer implemented method of training a model, using a machine learning process, to predict whether a transaction of a digital currency stored in a blockchain is fraudulent, comprises: unpacking (202) a block in the blockchain into a table comprising one or more rows of input and output data for a previous transaction stored in the block and aggregating (204) the one or more rows of input and output data to form an aggregated row of transaction data for the previous transaction. The method further comprises labelling (206) the aggregated row of transaction data for the previous transaction according to whether the previous transaction was fraudulent and using (208) the aggregated row of transaction data and the label as training data with which to train the model.

公开(公告)号：US20240420131A1

公开(公告)日：2024-12-19

申请号：US18748018

申请日：2024-06-19

Applicant: MASTERCARD INTERNATIONAL INCORPORATED

Inventor： Mohit Taneja ,  Jack Nicholls ,  James Conway ,  Nitish Kothale ,  Shannon Holland ,  Stephen Patrick Flinter ,  Weston Moran

IPC: G06Q20/38 ,  G06Q20/40

Abstract: A computer implemented method of decomposing a blockchain comprising transactions in digital currency for analysis and display is described. The method comprises first determining a range of blocks in the blockchain. Each block in the range of blocks in the blockchain is then unpacked into a table comprising one or more rows of input and output data for each transaction stored in the block. For the range of blocks in the blockchain, entity information and transaction information are then aggregated into a block analysis table. A node of a computing network and a computer program product adapted for implementation of such a method are also described.

公开(公告)号：US20240176598A1

公开(公告)日：2024-05-30

申请号：US18491923

申请日：2023-10-23

Applicant: MASTERCARD INTERNATIONAL INCORPORATED

Inventor： James Conway ,  Ngoc Minh Tran ,  Stephen Patrick Flinter

IPC: G06F8/35

CPC classification number: G06F8/35

Abstract: A system and method are provided for automating the development of artificial intelligence projects. The system includes a project store that enables the storage, searching and retrieval of existing artificial intelligence projects. The system also includes a data store that enables the storage, searching and retrieval of data sets. A search engine is provided that enables selected features to be applied to neural networks as part of the existing artificial intelligence projects. An artificial intelligence project server is also provided which utilizes a model training and validation module, a model interference module and an input module.

公开(公告)号：US20240037582A1

公开(公告)日：2024-02-01

申请号：US18265953

申请日：2021-12-08

Applicant: MASTERCARD INTERNATIONAL INCORPORATED

Inventor： Nicola Mariella ,  Stephen Patrick Flinter ,  James Conway ,  Quentin Bragard

IPC: G06Q30/0202 ,  G06Q30/0207 ,  G06Q10/0631

CPC classification number: G06Q30/0202 ,  G06Q30/0207 ,  G06Q10/0631

Abstract: A system and computer-implemented method for optimising, by a quantum computer, an allocation of opportunities to recipients comprising: determining a plurality of opportunities to be allocated; determining a plurality of recipients to be allocated at least one of the plurality of opportunities; determining a respective acceptance likelihood of each of the plurality of recipients accepting each of the plurality of opportunities; determining a first constraint associated with a cost acceptance of each of the plurality of opportunities by the plurality of recipients; and determining an optimised allocation of the opportunities to the recipients based on the respective likelihoods and the first constraint, in the quantum computer.