Credit Card Fraud Detection Using RoFormer Model With Relative Distance Rotating Encoding
This addresses fraud detection for financial systems like payment gateways, but it appears incremental as it builds on existing Transformer-based methods with a specific encoding enhancement.
The paper tackled credit card fraud detection by incorporating Relative Distance Rotating Encoding (ReDRE) into the RoFormer model, resulting in improved fraud detection through better capture of temporal dependencies and event relationships in transaction data.
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.