公开(公告)号：US20230297826A1

公开(公告)日：2023-09-21

申请号：US17697724

申请日：2022-03-17

Applicant: MASTERCARD INTERNATIONAL INCORPORATED

Inventor： Shiny John Shaju ,  Anil Kumar Surisetty ,  Alok Singh ,  Deepak Chaurasiya ,  Stephen Patrick Flinter ,  Quentin Bragard ,  Alvin Lee ,  I-Hsin Chuang

IPC: G06N3/08 ,  G06N7/00 ,  G06Q20/40

CPC classification number: G06N3/08 ,  G06N7/005 ,  G06Q20/405

Abstract: An aspect of the present disclosure is drawn to a method for managing a payment network, including: learning a transaction pattern of an account over time; generating a transfer function based on the transaction pattern; predicting information for the account based on the density function; changing a state of the account based on the predicted information; and automatically transmitting a notification of a feature to an owner of the account based on the changing of the state of the account, wherein the transfer function is a probability density function of a neural network and wherein the information includes a time of a future transaction linked to a correlated marker of the payment network.

公开(公告)号：US12010142B2

公开(公告)日：2024-06-11

申请号：US17469009

申请日：2021-09-08

Applicant: MASTERCARD INTERNATIONAL INCORPORATED

Inventor： Alok Singh ,  Nitish Kumar ,  Kanishka Kayathwal

IPC: H04L9/40 ,  G06N3/02 ,  G06N3/044 ,  G06N3/045 ,  G06N3/088

CPC classification number: H04L63/1483 ,  G06N3/044 ,  G06N3/045 ,  G06N3/088

Abstract: A generative adversarial network and a reinforcement learning system are combined to generate phishing emails with adaptive complexity. A plurality of phishing emails are obtained from a trained generative adversarial neural network, including a generator neural network and a discriminator neural network. A subset of phishing emails is selected, from the plurality of phishing emails, using a reinforcement learning system trained on user-specific behavior. One or more of the subset of phishing emails are sent to a user email account associated with a particular user. The reinforcement learning system is then adjusted based on user action feedback to the one or more of the subset of phishing emails.

公开(公告)号：US20230385849A1

公开(公告)日：2023-11-30

申请号：US17828945

申请日：2022-05-31

Applicant: Mastercard International Incorporated

Inventor： Athena Stacy-Nieto ,  Alok Singh ,  Nitish Kumar ,  Kaye Kirschner ,  Mahdi Jadaliha ,  Yuanzheng Du ,  Timothy McBride

IPC: G06Q30/00 ,  G16H10/60 ,  G06Q10/10 ,  G06N3/08 ,  G06N3/04

CPC classification number: G06Q30/0185 ,  G16H10/60 ,  G06Q10/10 ,  G06N3/088 ,  G06N3/0454

Abstract: A system and computer-implemented method for identifying fraudulent healthcare providers receives raw claims data from one or more data sources. The raw claims data includes claims associated with a selected healthcare provider. Each of the claims includes one or more claim lines. A first model is executed on the raw claims data. The first model determines a first score for the healthcare provider. A second model is executed on the raw claims data. The second model determines a second score for the healthcare provider. In addition, a third model is executed on the raw claims data. The third model determines a third score for the healthcare provider. A final provider-level risk score is determined for the healthcare provider based on the first, second, and third scores.

公开(公告)号：US20230072129A1

公开(公告)日：2023-03-09

申请号：US17901262

申请日：2022-09-01

Applicant: Mastercard International Incorporated

Inventor： Nitish Kumar ,  Alok Singh ,  Deepak Chaurasiya ,  Kushagra Agarwal

IPC: G06Q40/08

Abstract: Computer implemented method for detecting procedure and diagnosis code anomalies in provider service data. The method includes generating a co-occurrence adjacency matrix from service provider data of a plurality of providers. The adjacency matrix includes counts of the number of co-occurrences of a plurality of diagnoses and a plurality of procedures in the service provider data. A plurality of graph node embeddings is created based on the adjacency matrix. Each of the plurality of graph node embeddings is assigned to one of a plurality of clusters. A health insurance claim is evaluated for excessive billing based on how many of the plurality of clusters is represented in the claim.