Financial Transaction Anomaly Detection Based on Transformer Model

Nowadays, with the rapid development of financial markets and explosive growth of transaction data, abnormal detection of financial transactions has become a key link in ensuring financial security and stability. Traditional anomaly detection methods have limitations when dealing with complex financial transaction data. This article delves into the financial transaction anomaly detection method based on the Transformer model, elaborating on the principles of the Transformer model, including its unique attention mechanism, analyzing the characteristics of financial transaction data, and exploring how to adapt data to the Transformer model. The performance of Transformer based models and traditional detection methods in financial transaction anomaly detection tasks was compared through experiments. The results showed that Transformer based models have higher accuracy and robustness, providing a more effective solution for financial transaction anomaly detection.