Attention-Enhanced Time Series Anomaly Detection for Financial Risk Early Warning: A Deep Learning Approach

Abstract

Financial market volatility and risk propagation present significant challenges for timely intervention and loss prevention. This paper proposes an attention-enhanced deep learning framework for detecting anomalous patterns in financial time series data, enabling early warning of potential risks. The framework integrates temporal convolutional networks with multi-head self-attention mechanisms to capture both short-term fluctuations and long-range dependencies in transaction sequences. Comprehensive experiments on real-world financial datasets demonstrate substantial improvements in detection accuracy, with F1-scores reaching 94.3% for fraud identification and 91.7% for market manipulation detection. The proposed approach achieves a 37% reduction in false positive rates compared to traditional statistical methods while maintaining computational efficiency suitable for real-time deployment. Performance analysis across multiple financial instruments reveals consistent effectiveness in identifying emerging risk patterns 48-72 hours before significant market events. The findings provide valuable insights for financial institutions seeking to enhance their risk management capabilities through advanced analytics.