Synthetic Data for Payment Systems: AI-Powered Privacy-Preserving Testing

In modern banking, ensuring that new payment systems operate accurately and securely requires extensive testing. However, testing with real-world data introduces privacy risks, and synthetic data offers a promising alternative. This paper explores the potential of Generative AI for producing realistic, privacy‑compliant synthetic transaction data. The proposed approach addresses challenges such as data privacy, diverse dataset creation, and the ability to simulate rare or edge-case scenarios—thus enhancing the robustness of payment systems.

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Publication Details

Journal
International Journal of Management Technology
Published
2025-01-15
DOI
https://doi.org/10.37745/ijmt.2013/vol12n16776
Primary Topic
Privacy-Preserving Technologies in Data
Type
article
Field-Weighted Citation Impact
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article

Synthetic Data for Payment Systems: AI-Powered Privacy-Preserving Testing

Prajwalkumar B Bhatkar
International Journal of Management Technology
Privacy-Preserving Technologies in Data
article

Synthetic Data for Payment Systems: AI-Powered Privacy-Preserving Testing

Prajwalkumar B Bhatkar
article en

Abstract

In modern banking, ensuring that new payment systems operate accurately and securely requires extensive testing. However, testing with real-world data introduces privacy risks, and synthetic data offers a promising alternative. This paper explores the potential of Generative AI for producing realistic, privacy‑compliant synthetic transaction data. The proposed approach addresses challenges such as data privacy, diverse dataset creation, and the ability to simulate rare or edge-case scenarios—thus enhancing the robustness of payment systems.

International Journal of Management TechnologyVol. 12(1)
Bank of America (US), Centre for European Reform (GB)
Openalex Percentile: Top 98%
Privacy-Preserving Technologies in Data
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