公开(公告)号：US20230051764A1

公开(公告)日：2023-02-16

申请号：US17401048

申请日：2021-08-12

Applicant: MASTERCARD INTERNATIONAL INCORPORATED

Inventor： Shreyansh SINGH ,  Gaurav Dhama ,  Ankur Arora ,  Kanishka Kayathwal ,  Jessica Carta ,  Ganesh Nagendra Prasad

IPC: G06Q30/02 ,  G06F40/30 ,  G06F40/284 ,  G06K9/62

Abstract: The disclosure relates to AI-based machine-learning and natural language modeling to identify semantic similarities between sets of content having natural language text. For example, a system may generate a relevance classification that indicates whether content such as articles are non-specifically relevant to charities without identifying a particular charity. If the content is non-specifically relevant to charities, the system may apply a natural language model to generate sentence embeddings based on the content and determine a level similarity between the sentence embeddings and a query embedding generated from a charity query. The charity query may itself be generated from a full description of the charity through an encoder-decoder architecture with reinforcement learning.

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：US20230186311A1

公开(公告)日：2023-06-15

申请号：US18078895

申请日：2022-12-09

Applicant: Mastercard International Incorporated

Inventor： Siddharth Vimal ,  Gaurav Dhama ,  Kanishka Kayathwal ,  Nishant Kumar

IPC: G06Q20/40

CPC classification number: G06Q20/4016

Abstract: Various embodiments relate to methods and systems for generating adversarial samples. The method performed by a server system includes accessing a set of payment transaction samples from transaction database. The method includes initializing a plurality of encoders, weights of each of the plurality of encoders being randomly initialized. Further, the method includes computing a set of initial adversarial samples using the plurality of encoders based on the set of payment transaction samples. Further, the method includes optimizing the plurality of encoders to generate a plurality of evolved encoders. Further, method includes computing a plurality of fitness scores for the plurality of evolved encoders. Further, the method includes determining a top evolved encoder from the plurality of evolved encoders based on the plurality of fitness scores. Further, the method includes generating a set of final adversarial samples using the top evolved encoder based on the set of payment transaction samples.

公开(公告)号：US12136112B2

公开(公告)日：2024-11-05

申请号：US17401048

申请日：2021-08-12

Applicant: MASTERCARD INTERNATIONAL INCORPORATED

Inventor： Shreyansh Singh ,  Gaurav Dhama ,  Ankur Arora ,  Kanishka Kayathwal ,  Jessica Carta ,  Ganesh Nagendra Prasad

IPC: G06Q30/02 ,  G06F16/24 ,  G06F16/2457 ,  G06F18/21 ,  G06F18/22 ,  G06F40/20 ,  G06F40/284 ,  G06F40/30 ,  G06Q30/0279

Abstract: The disclosure relates to AI-based machine-learning and natural language modeling to identify semantic similarities between sets of content having natural language text. For example, a system may generate a relevance classification that indicates whether content such as articles are non-specifically relevant to charities without identifying a particular charity. If the content is non-specifically relevant to charities, the system may apply a natural language model to generate sentence embeddings based on the content and determine a level similarity between the sentence embeddings and a query embedding generated from a charity query. The charity query may itself be generated from a full description of the charity through an encoder-decoder architecture with reinforcement learning.

公开(公告)号：US20230206241A1

公开(公告)日：2023-06-29

申请号：US18145594

申请日：2022-12-22

Applicant: MASTERCARD INTERNATIONAL INCORPORATED

Inventor： Kanishka Kayathwal ,  Gaurav Dhama ,  Hardik Wadhwa ,  Shreyansh Singh ,  Siddharth Vimal ,  Abhishek Garg ,  Ankur Arora

IPC: G06Q20/40 ,  G06Q20/06

CPC classification number: G06Q20/4016 ,  G06Q20/065

Abstract: Embodiments provide artificial intelligence methods and systems for evaluating vulnerability risks of issuer authorization system. Method performed by a server system includes accessing a set of payment transaction data including subset of fraudulent transaction data. Method includes generating via a machine learning model, set of synthetic transaction data based on the subset of fraudulent transaction data. Method includes accessing set of historical card velocity features and collating the set of synthetic transaction data and the set of historical card velocity features to generate set of enriched synthetic transaction data. Method includes extracting via a classifier, subset of feasible fraudulent transaction data from the set of enriched synthetic transaction data. Method includes generating simulated authorization model based on the set of payment transaction data. Method includes classifying via simulated authorization model, each enriched synthetic transaction from the subset of feasible fraudulent transaction data as one of fraudulent transaction and non-fraudulent transaction.