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1.发明公开公开(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.
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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
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.
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公开(公告)号：US11838301B2
公开(公告)日：2023-12-05
申请号：US17243201
申请日：2021-04-28
Applicant: MASTERCARD INTERNATIONAL INCORPORATED
Inventor： Sonali Syngal , Kanishk Goyal , Suhas Powar , Ankur Saraswat , Debasmita Das , Yatin Katyal
IPC: H04L29/06 , G06F16/21 , G06F16/2458 , H04L9/40
CPC classification number: H04L63/1416 , G06F16/219 , G06F16/2462 , H04L63/102 , H04L63/104 , H04L63/1425
Abstract: The disclosure herein describes a system and method for predictive identification of breached entities. Identification number and expiration date pairs associated with compromised records in a source file are analyzed to identify a set of candidate entities having records at least partially matching the source file data pairs having events occurring during a selected time period. Probability vectors are calculated for records associated with each identified entity. A divergence value is calculated which represents a distance between probability distribution vectors for each entity and probability distribution vectors for the source file. A predicted breached entity is identified based on the divergence values. The predicted breached entity is notified of the predicted breach. The notification can include an identification of the breached entity, identification of breached records, predicted time of breach, and/or a recommendation to take action to mitigate the predicted breach.
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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
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.
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