Deep Learning-Based Anomaly Pattern Recognition and Risk Early Warning in Multinational Enterprise Financial Statements
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Abstract
This research presents a comprehensive deep learning framework for anomaly pattern recognition and risk early warning in multinational enterprise financial statements. The proposed CNN-LSTM hybrid architecture addresses the complexity of cross-border financial data analysis by integrating convolutional neural networks with long short-term memory mechanisms. The framework employs multi-dimensional feature extraction techniques to identify subtle anomalous patterns across diverse currency environments and regulatory frameworks. Experimental validation demonstrates superior performance compared to traditional statistical methods, achieving 94.7% accuracy in anomaly detection with a false positive rate of 3.2%. The intelligent early warning system incorporates real-time monitoring capabilities and dynamic threshold adjustment algorithms, enabling proactive risk management for regulatory authorities. The research contributes to financial stability through enhanced transparency and automated detection of potential fraud or misstatement patterns in global enterprise operations. Implementation results across 500 multinational corporations validate the framework's effectiveness in diverse industry sectors and geographical regions.
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