Hyphatia: A Card-Not-Present Fraud Detection System Based on Self-Supervised Tabular Learning
In order to conduct credit card fraud, having only the payment card information of the victim it is possible to fake its identity and buy on e-commerce platforms. This type of fraud is known as Card-Not-Present and shows in the form of chargebacks, projecting billion-dollar losses worldwide in the c...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of the Computer Society |
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| Online Access: | https://ieeexplore.ieee.org/document/11004629/ |
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| author | Josue Genaro Almaraz-Rivera Jose Antonio Cantoral-Ceballos Juan Felipe Botero Francisco Javier MunOz Brian David Martinez |
| author_facet | Josue Genaro Almaraz-Rivera Jose Antonio Cantoral-Ceballos Juan Felipe Botero Francisco Javier MunOz Brian David Martinez |
| author_sort | Josue Genaro Almaraz-Rivera |
| collection | DOAJ |
| description | In order to conduct credit card fraud, having only the payment card information of the victim it is possible to fake its identity and buy on e-commerce platforms. This type of fraud is known as Card-Not-Present and shows in the form of chargebacks, projecting billion-dollar losses worldwide in the coming years. The IEEE-CIS dataset has emerged as a strong option for creating and validating smart detection systems against this problem. In this work, we propose a solution, Hyphatia, where Self-Supervised Learning is implemented for tabular data based on SubTab. Our model outperforms XGBoost by 2.14% AUROC, detecting 67.44% of the fraud cases in the IEEE-CIS. This pioneering experimentation prioritizes those features that are not obfuscated. Furthermore, beyond providing just classification metrics, we also share time performance and feature importance calculations for explainability. To the best of our knowledge, this is one of the first works in the literature using Self-Supervised Learning for the problem of credit card fraud detection, specifically using the Self-Supervised Tabular Learning approach. |
| format | Article |
| id | doaj-art-96b2833cce0b4181918ea48b7eb214cd |
| institution | OA Journals |
| issn | 2644-1268 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Computer Society |
| spelling | doaj-art-96b2833cce0b4181918ea48b7eb214cd2025-08-20T02:33:00ZengIEEEIEEE Open Journal of the Computer Society2644-12682025-01-01681282110.1109/OJCS.2025.357060011004629Hyphatia: A Card-Not-Present Fraud Detection System Based on Self-Supervised Tabular LearningJosue Genaro Almaraz-Rivera0https://orcid.org/0000-0001-8343-4530Jose Antonio Cantoral-Ceballos1https://orcid.org/0000-0001-5597-939XJuan Felipe Botero2https://orcid.org/0000-0002-7072-8924Francisco Javier MunOz3https://orcid.org/0009-0009-5939-2137Brian David Martinez4https://orcid.org/0009-0005-9544-0328Tecnologico de Monterrey, School of Engineering and Sciences, Monterrey, MexicoTecnologico de Monterrey, School of Engineering and Sciences, Monterrey, MexicoUniversidad de Antioquia, Electronics and Telecommunications Engineering Department, Medellin, ColombiaAligo Defensores Informaticos S.A.S., Medellin, ColombiaAligo Defensores Informaticos S.A.S., Medellin, ColombiaIn order to conduct credit card fraud, having only the payment card information of the victim it is possible to fake its identity and buy on e-commerce platforms. This type of fraud is known as Card-Not-Present and shows in the form of chargebacks, projecting billion-dollar losses worldwide in the coming years. The IEEE-CIS dataset has emerged as a strong option for creating and validating smart detection systems against this problem. In this work, we propose a solution, Hyphatia, where Self-Supervised Learning is implemented for tabular data based on SubTab. Our model outperforms XGBoost by 2.14% AUROC, detecting 67.44% of the fraud cases in the IEEE-CIS. This pioneering experimentation prioritizes those features that are not obfuscated. Furthermore, beyond providing just classification metrics, we also share time performance and feature importance calculations for explainability. To the best of our knowledge, this is one of the first works in the literature using Self-Supervised Learning for the problem of credit card fraud detection, specifically using the Self-Supervised Tabular Learning approach.https://ieeexplore.ieee.org/document/11004629/Card-not-present transactionscredit card fraudIEEE-CIS datasetself-supervised learningself-supervised tabular learningSubTab |
| spellingShingle | Josue Genaro Almaraz-Rivera Jose Antonio Cantoral-Ceballos Juan Felipe Botero Francisco Javier MunOz Brian David Martinez Hyphatia: A Card-Not-Present Fraud Detection System Based on Self-Supervised Tabular Learning IEEE Open Journal of the Computer Society Card-not-present transactions credit card fraud IEEE-CIS dataset self-supervised learning self-supervised tabular learning SubTab |
| title | Hyphatia: A Card-Not-Present Fraud Detection System Based on Self-Supervised Tabular Learning |
| title_full | Hyphatia: A Card-Not-Present Fraud Detection System Based on Self-Supervised Tabular Learning |
| title_fullStr | Hyphatia: A Card-Not-Present Fraud Detection System Based on Self-Supervised Tabular Learning |
| title_full_unstemmed | Hyphatia: A Card-Not-Present Fraud Detection System Based on Self-Supervised Tabular Learning |
| title_short | Hyphatia: A Card-Not-Present Fraud Detection System Based on Self-Supervised Tabular Learning |
| title_sort | hyphatia a card not present fraud detection system based on self supervised tabular learning |
| topic | Card-not-present transactions credit card fraud IEEE-CIS dataset self-supervised learning self-supervised tabular learning SubTab |
| url | https://ieeexplore.ieee.org/document/11004629/ |
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