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：US20220374684A1

公开(公告)日：2022-11-24

申请号：US17746661

申请日：2022-05-17

Applicant: MASTERCARD INTERNATIONAL INCORPORATED

Inventor： Sonali Syngal ,  Debasmita Das ,  Soumyadeep Ghosh ,  Yatin Katyal ,  Kandukuri Karthik ,  Ankur Saraswat

IPC: G06N3/04 ,  G06V10/774 ,  G06V10/82

Abstract: Embodiments provide electronic methods and systems for improving edge case classifications. The method performed by a server system includes accessing an input sample dataset including first labeled training data associated with a first class, and second labeled training data associated with a second class, from a database. Method includes executing training of a first autoencoder and a second autoencoder based on the first and second labeled training data, respectively. Method includes providing the first and second labeled training data along with unlabeled training data accessed from the database to the first and second autoencoders. Method includes calculating a common loss function based on a combination of a first reconstruction error associated with the first autoencoder and a second reconstruction error associated with the second autoencoder. Method includes fine-tuning the first autoencoder and the second autoencoder based on the common loss function.

公开(公告)号：US20250165864A1

公开(公告)日：2025-05-22

申请号：US18948401

申请日：2024-11-14

Applicant: Mastercard International Incorporated

Inventor： Soumyadeep Ghosh ,  Harsimran Bhasin ,  Maneet Singh

IPC: G06N20/00

Abstract: Methods and systems for re-training a Machine Learning (ML) model using predicted features from a training dataset are disclosed. A method performed by a server system includes accessing a training feature set and a testing feature set from a database. In response to identifying an inclusion of at least one new feature in the testing feature set, the method includes training a surrogate ML model to predict a value for the new feature based on the testing feature set and determining, by the surrogate ML model, a predicted value for the new feature for each training data sample in a training dataset based on the training feature set. The method further includes generating a new training feature set for each training data sample based on the predicted value and the training feature set. The method includes re-training the ML model based on the new training feature for each data sample.