公开(公告)号：US20230376874A1

公开(公告)日：2023-11-23

申请号：US18031344

申请日：2021-10-06

Applicant: MASTERCARD INTERNATIONAL INCORPORATED

Inventor： Quentin Bragard ,  James Thomas Conway ,  Nitish Kothale

IPC: G06Q10/0635 ,  G06Q40/02

CPC classification number: G06Q10/0635 ,  G06Q40/02

Abstract: An apparatus for determining a level of risk that a future transfer will exceed a level of reserve is provided by the present disclosure, the apparatus comprising circuitry configured to: obtain data of transfers between financial institutions which have occurred at a number of instances of time, the data including transfer amounts; apply a predictive model to the data to obtain a prediction of the transfer amount at each instance of time; determine a maximum residual between the prediction of the transfer amount and the transfer amount at each instance of time; model the maximum residual for each instance of time using a generalised extreme value distribution to obtain a distribution function; and determine the level of risk that a future transfer between financial institutions will exceed the level of reserve based on the distribution function which has been obtained.

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.