Fraud detection methods and systems based on evolution-based black-box attack models
Summary
USPTO granted Mastercard International Incorporated Patent US12597034B2 for fraud detection methods using evolution-based black-box attack models. The patent covers systems that generate adversarial samples through encoder optimization to identify fraud patterns in payment transactions. The patent contains 18 claims and names four inventors including Siddharth Vimal and Gaurav Dhama.
What changed
USPTO issued Patent US12597034B2 to Mastercard International Incorporated for methods and systems generating adversarial samples to detect fraud. The patented technology includes accessing payment transaction samples, initializing encoders with random weights, computing initial adversarial samples, optimizing encoders through evolutionary algorithms, computing fitness scores, and generating final adversarial samples for fraud detection. The patent relates to CPC classification G06Q 20/4016 covering payment transaction security.
This patent grant establishes Mastercard's intellectual property rights in AI-driven fraud detection using adversarial machine learning techniques. Competitors developing payment fraud detection systems should ensure their solutions do not infringe these claims. Financial technology companies and payment processors may need to consider licensing implications when developing similar fraud prevention technologies.
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Apr 7, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
Fraud detection methods and systems based on evolution-based black-box attack models
Grant US12597034B2 Kind: B2 Apr 07, 2026
Assignee
Mastercard International Incorporated
Inventors
Siddharth Vimal, Gaurav Dhama, Kanishka Kayathwal, Nishant Kumar
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.
CPC Classifications
G06Q 20/4016
Filing Date
2022-12-09
Application No.
18078895
Claims
18
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