标题： 使用相对距离旋转编码的RoFormer模型进行信用卡欺诈检测 显示英文标题

标题： Credit Card Fraud Detection Using RoFormer Model With Relative Distance Rotating Encoding

摘要： 欺诈检测是金融系统必须解决的最重要的挑战之一。 检测欺诈交易对于支付网关公司如Flow Payment来说至关重要，这些公司每月处理数百万笔交易，并需要强大的安全措施来减轻财务风险。 在提高交易授权率的同时减少欺诈对于提供良好的用户体验和建立可持续的业务至关重要。 因此，为了在这个行业中取得成功，任何公司都需要持续的研究和投资来发现新的和改进的欺诈检测方法。 在本工作中，我们通过在RoFormer模型中引入相对距离旋转编码（ReDRE），提出了一种新的交易欺诈检测方法。 使用ReDRE进行角度旋转的结合增强了Transformer中时间序列数据的表征，从而通过更好地捕捉时间依赖性和事件关系来提高欺诈检测效果。 显示英文摘要

摘要： 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.