用旋转编码提升Transformer对交易时间序列的建模，更好识别信用卡欺诈
Credit Card Fraud Detection Using RoFormer Model With Relative Distance Rotating Encoding
- 在RoFormer中引入相对距离旋转编码，增强时序特征表达
- 相比基线模型，欺诈检测准确率显著提升，具体数值未在摘要中提及
- 适合关注金融安全与时序建模的AI研究者和工程师
欺诈检测是金融系统必须应对的关键挑战。对于每月处理数百万笔交易的支付网关公司（如Flow Payment）而言，检测欺诈交易对降低财务风险至关重要。在提高交易授权率的同时减少欺诈，对提升用户体验和构建可持续业务至关重要。因此，持续研究和投资新型高效的欺诈检测方法，是企业在该领域取得成功的关键。本文提出一种新方法，通过在RoFormer模型中引入相对距离旋转编码（ReDRE），增强Transformer对时间序列数据的表征能力，从而更有效地捕捉时间依赖性和事件关联性，提升欺诈检测性能。
原文摘要 · Abstract (English)
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.
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