公开(公告)号：US20250068911A1

公开(公告)日：2025-02-27

申请号：US18455546

申请日：2023-08-24

Applicant: MASTERCARD INTERNATIONAL INCORPORATED

Inventor： Aakarsh Malhotra ,  Sonia Gupta ,  Akshay Sethi ,  Siddhartha Asthana

IPC: G06N3/084

Abstract: Methods and server systems for mitigating negative transfer in Multi-Task Learning (MTL) are described herein. Method performed by server system includes accessing training dataset and training multi-task machine learning (MTML) model based on performing operations. The operations includes initializing MTML model based on model parameters. Then, computing task affinity metric for each task of a set of tasks based on determining an affinity between each task and one or more tasks. Then, computing task-specific activation probability for each task based on task affinity metric corresponding to each task. Then, activating subset of tasks when task-specific activation probability corresponding to each individual task from the subset of tasks is lower than predefined threshold. Further, processing, via MTML model, training dataset by performing subset of tasks to compute outputs. Furthermore, generating task-specific losses for subset of tasks based on outputs and training dataset. Thereafter, optimizing model parameters based on back-propagating task-specific losses.

公开(公告)号：US20230126708A1

公开(公告)日：2023-04-27

申请号：US18049171

申请日：2022-10-24

Applicant: MASTERCARD INTERNATIONAL INCORPORATED

Inventor： Hardik Wadhwa ,  Anubhav Gupta ,  Aditya Singh ,  Siddhartha Asthana ,  Ankur Arora

IPC: G06Q20/40 ,  G06Q20/06

Abstract: Embodiments provide methods and systems for removing temporal biases in classification tasks. Method performed by server system includes accessing a transaction graph associated with a particular time duration and determining a set of local features and aggregate features associated with each node based on labeled data. Method includes generating via a machine learning model, a set of intermediate node representations associated with each of the plurality of nodes based on the set of local features and the set of aggregate features. Method includes generating via a fraud model and a timestep model, a fraud classification loss, and a timestep classification loss based on the set of intermediate node representations. Method includes determining an adversarial loss value based on the fraud classification loss and the timestep classification loss. Method includes determining a set of optimized parameters for the machine learning model based on the adversarial loss value.

公开(公告)号：US20250068910A1

公开(公告)日：2025-02-27

申请号：US18455518

申请日：2023-08-24

Applicant: MASTERCARD INTERNATIONAL INCORPORATED

Inventor： Aakarsh Malhotra ,  Akshay Sethi ,  Sonia Gupta ,  Siddhartha Asthana

IPC: G06N3/084 ,  G06N3/0475

Abstract: Methods and server systems for generating task-agnostic representations for nodes in bipartite graph are described herein. Method performed by server system includes accessing bipartite graph including first set of nodes and second set of nodes. Herein, set of edges exist between first and second set of nodes. Method includes performing for each node of first and second set of nodes: identifying a natural neighbor node, the natural neighbor node being a two-hop neighbor node from the each node, Then, generating temporary representation for one-hop neighbor node based on set of features corresponding to the one-hop neighbor node Then, generating temporary neighbor node based on temporary representation for the one-hop neighbor node. Then, generating augmented neighborhood based on the natural node and the temporary neighbor node, and then determining via machine learning model, task-agnostic representation for the each node based on augmented neighborhood.

公开(公告)号：US20240403369A1

公开(公告)日：2024-12-05

申请号：US18733518

申请日：2024-06-04

Applicant: MASTERCARD INTERNATIONAL INCORPORATED

Inventor： Akshay Sethi ,  Ayush Agarwal ,  Nancy Agrawal ,  Siddhartha Asthana ,  Sonia Gupta

IPC: G06F16/906 ,  G06F16/901

Abstract: Methods and systems for training artificial intelligence (AI)-based models using limited labeled data are disclosed. The method performed by a server system includes accessing a tabular dataset including tabular data that further labeled data and unlabeled data. Method includes generating labeled features including labeled numerical features and labeled categorical features based on the labeled data and generating unlabeled features including unlabeled numerical features and unlabeled categorical features based on the unlabeled data. Method includes determining, via a first transformer model, a contextual numerical embeddings based on the labeled numerical features and the unlabeled numerical features. Method includes determining, via a second transformer model, a contextual categorical embeddings based on the labeled categorical features and the unlabeled categorical features. Method includes generating a concatenated embeddings based on concatenating the contextual numerical embeddings and the contextual categorical embeddings. Method includes generating a third transformer model based on the concatenated embeddings.

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：US20220366493A1

公开(公告)日：2022-11-17

申请号：US17739056

申请日：2022-05-06

Applicant: Mastercard International Incorporated

Inventor： Ankur Arora ,  Lalasa Dheekollu ,  Siddhartha Asthana ,  Amit Kumar ,  Smriti Gupta ,  Ankur Saraswat ,  Kandukuri Karthik ,  Kushagra Agarwal ,  Himanshi Charotia ,  Anket Prakash Hirulkar ,  Janu Verma ,  Kanishk Goyal ,  Gaurav Dhama

IPC: G06Q40/02

Abstract: Embodiments provide methods and systems for predicting overall account-level risks of cardholders. The method performed by server system includes accessing payment transaction data associated with a cardholder from a transaction database. Method includes generating a set of transaction features based on a set of transaction indicators. The method includes determining a plurality of network risk scores associated with the cardholder based on the set of transaction features and a set of trained machine learning models. The plurality of network risk scores includes a payment capacity risk score, a contactless payment risk score, and a set of account-level risk scores. The method includes aggregating the plurality of network risk scores to calculate an overall account risk score associated with the cardholder based on a statistical model. The method also includes transmitting a notification to the issuer server associated with the cardholder based on the overall account risk score.

公开(公告)号：US20220374927A1

公开(公告)日：2022-11-24

申请号：US17750380

申请日：2022-05-22

Applicant: Mastercard International Incorporated

Inventor： Ankur Arora ,  Harsimran Bhasin ,  Siddhartha Asthana ,  Hardik Wadhwa ,  Tanmoy Bhowmik ,  Karamjit Singh

IPC: G06Q30/02

Abstract: Embodiments provide methods and systems for predicting panic situation in a region and detecting panic states of merchants in the region. Method performed by server system includes accessing payment transaction data associated with merchants from transaction database and identifying panic trigger indicating panic situation in region based on transaction features associated with merchants. In response to identifying panic trigger, method includes generating transaction features based on payment transactions of merchant over time duration and determining association between merchant and merchant cluster based on transaction features associated with merchant. Method includes predicting time-series transaction data associated with merchant based on deep neural network model and merchant cluster associated with merchant, and calculating error between predicted time-series transaction data and real time-series transaction data associated with merchant. Method further includes identifying panic state for merchant based on error between predicted time-series transaction data and real time-series transaction data of merchant.