Credit Card Fraud Detection Using RoFormer Model With Relative Distance Rotating Encoding
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915386103955456 |
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| author | Reyes, Kevin Cortez, Vasco |
| author_facet | Reyes, Kevin Cortez, Vasco |
| contents | Fraud detection is one of the most important challenges that financial systems must address. Detecting fraudulent transactions is critical for payment gateway companies like Flow Payment, which process millions of transactions monthly and require robust security measures to mitigate financial risks. Increasing transaction authorization rates while reducing fraud is essential for providing a good user experience and building a sustainable business. For this reason, discovering novel and improved methods to detect fraud requires continuous research and investment for any company that wants to succeed in this industry. In this work, we introduced a novel method for detecting transactional fraud by incorporating the Relative Distance Rotating Encoding (ReDRE) in the RoFormer model. The incorporation of angle rotation using ReDRE enhances the characterization of time series data within a Transformer, leading to improved fraud detection by better capturing temporal dependencies and event relationships. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_09385 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Credit Card Fraud Detection Using RoFormer Model With Relative Distance Rotating Encoding Reyes, Kevin Cortez, Vasco Neural and Evolutionary Computing Machine Learning Fraud detection is one of the most important challenges that financial systems must address. Detecting fraudulent transactions is critical for payment gateway companies like Flow Payment, which process millions of transactions monthly and require robust security measures to mitigate financial risks. Increasing transaction authorization rates while reducing fraud is essential for providing a good user experience and building a sustainable business. For this reason, discovering novel and improved methods to detect fraud requires continuous research and investment for any company that wants to succeed in this industry. In this work, we introduced a novel method for detecting transactional fraud by incorporating the Relative Distance Rotating Encoding (ReDRE) in the RoFormer model. The incorporation of angle rotation using ReDRE enhances the characterization of time series data within a Transformer, leading to improved fraud detection by better capturing temporal dependencies and event relationships. |
| title | Credit Card Fraud Detection Using RoFormer Model With Relative Distance Rotating Encoding |
| topic | Neural and Evolutionary Computing Machine Learning |
| url | https://arxiv.org/abs/2507.09385 |