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：US20240119457A1

公开(公告)日：2024-04-11

申请号：US18482733

申请日：2023-10-06

Applicant: MASTERCARD INTERNATIONAL INCORPORATED

Inventor： Smriti Gupta ,  Adarsh Patankar ,  Akash Choudhary ,  Alekhya Bhatraju ,  Ammar Ahmad Khan ,  Amrita Kundu ,  Ankur Saraswat ,  Anubhav Gupta ,  Awanish Kumar ,  Ayush Agarwal ,  Brian M. McGuigan ,  Debasmita Das ,  Deepak Yadav ,  Diksha Shrivastava ,  Garima Arora ,  Gaurav Dhama ,  Gaurav Oberoi ,  Govind Vitthal Waghmare ,  Hardik Wadhwa ,  Jessica Peretta ,  Kanishk Goyal ,  Karthik Prasad ,  Lekhana Vusse ,  Maneet Singh ,  Niranjan Gulla ,  Nitish Kumar ,  Rajesh Kumar Ranjan ,  Ram Ganesh V ,  Rohit Bhattacharya ,  Rupesh Kumar Sankhala ,  Siddhartha Asthana ,  Soumyadeep Ghosh ,  Sourojit Bhaduri ,  Srijita Tiwari ,  Suhas Powar ,  Susan Skelsey

IPC: G06Q20/40

CPC classification number: G06Q20/4016

Abstract: Methods and server systems for computing fraud risk scores for various merchants associated with an acquirer described herein. The method performed by a server system includes accessing merchant-related transaction data including merchant-related transaction indicators associated with a merchant from a transaction database. Method includes generating a merchant-related transaction features based on the merchant-related indicators. Method includes generating via risk prediction models, for a payment transaction with the merchant, merchant health and compliance risk scores, merchant terminal risk scores, merchant chargeback risk scores, and merchant activity risk scores based on the merchant-related transaction features. Method includes facilitating transmission of a notification message to an acquirer server associated with the merchant. The notification message includes the merchant health and compliance risk scores, the merchant terminal risk scores, the merchant chargeback risk scores, and the merchant activity risk scores.

公开(公告)号：US12217263B2

公开(公告)日：2025-02-04

申请号：US17739045

申请日：2022-05-06

Applicant: Mastercard International Incorporated

Inventor： Bhargav Pandillapalli ,  Rajesh Kumar Ranjan ,  Ankur Saraswat ,  Kshitij Gangwar ,  Kamal Kant ,  Sonali Syngal ,  Suhas Powar ,  Debasmita Das ,  Pritam Kumar Nath ,  Nishant Pant ,  Yatin Katyal ,  Nitish Kumar ,  Karamjit Singh

IPC: G06Q20/40 ,  G06Q20/36 ,  G06Q20/38 ,  G06Q40/03

Abstract: Embodiments provide artificial intelligence-based methods and systems for predicting account-level risk scores associated with cardholders. Method performed by server system includes accessing payment transaction data and cardholder risk data associated with cardholder. The payment transaction data includes transaction variables associated with past payment transactions performed at Point of Interaction (POI) terminals within a particular time window. Method includes generating cardholder profile data based on the transaction variables and the cardholder risk data. Method includes determining account-level risk scores associated with the cardholder based on cardholder profile data. Each account-level risk score of account-level risk scores is determined by a trained machine learning model. The account-level risk scores include a wallet reload risk score, an account reissuance risk score, and a transaction channel risk score. Further, the method includes transmitting a recommendation message to an issuer server associated with the cardholder based on the account-level risk 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.

公开(公告)号：US20220358508A1

公开(公告)日：2022-11-10

申请号：US17739045

申请日：2022-05-06

Applicant: Mastercard International Incorporated

Inventor： Bhargav Pandillapalli ,  Rajesh Kumar Ranjan ,  Ankur Saraswat ,  Kshitij Gangwar ,  Kamal Kant ,  Sonali Syngal ,  Suhas Powar ,  Debasmita Das ,  Pritam Kumar Nath ,  Nishant Pant ,  Yatin Katyal ,  Nitish Kumar ,  Karamjit Singh

IPC: G06Q20/40 ,  G06Q20/38 ,  G06Q20/36

Abstract: Embodiments provide artificial intelligence-based methods and systems for predicting account-level risk scores associated with cardholders. Method performed by server system includes accessing payment transaction data and cardholder risk data associated with cardholder. The payment transaction data includes transaction variables associated with past payment transactions performed at Point of Interaction (POI) terminals within a particular time window. Method includes generating cardholder profile data based on the transaction variables and the cardholder risk data. Method includes determining account-level risk scores associated with the cardholder based on cardholder profile data. Each account-level risk score of account-level risk scores is determined by a trained machine learning model. The account-level risk scores include a wallet reload risk score, an account reissuance risk score, and a transaction channel risk score. Further, the method includes transmitting a recommendation message to an issuer server associated with the cardholder based on the account-level risk scores.