Application Research of Deep Learning-Based Large-Scale Time Series Intelligent Analysis in Financial Risk Control

Abstract

In recent years, with the acceleration of digital transformation in global financial markets, financial transaction data has shown exponential growth. Traditional time series analysis methods face problems such as complex feature engineering and insufficient pattern capture ability when processing high-dimensional and nonlinear financial data. This study proposes a three-dimensional hybrid deep learning model based on GCN-LSTM-CLUSTING to address this challenge. By integrating graph convolutional networks (GCN), long short-term memory networks (LSTM), and clustering analysis techniques, a multi-level and multi perspective financial risk assessment framework is constructed. This model innovatively integrates complex network relationships, time series dynamic features, and risk event pattern recognition among financial institutions, achieving full chain automation processing from data collection, feature extraction to risk decision-making. On the theoretical level, the model combines the basic theory of deep learning, time series analysis methods, and financial risk control theory to form a systematic modeling ability for nonlinear and high-dimensional financial data; On the technical level, the GCN module captures the correlation characteristics of market participants, the LSTM module processes non-stationary sequences such as asset size fluctuations, and the CLUSTING module implements clustering analysis of risk events, significantly improving the robustness of the model in complex market environments.

The experimental results show that the 3D model has an accuracy improvement of 12% -18% compared to traditional methods in predicting abnormal fluctuations in asset size and identifying systemic risk transmission pathways. In the risk control scenario of oil product sales customers, the model improves the accuracy of identifying high-risk customers to 87.3% and reduces the false alarm rate to 5.2%; The application of quantitative stock selection strategy shows that the annualized return rate of the strategy reached 18.7%, an increase of 6.4 percentage points compared to traditional methods, and the maximum drawdown decreased by 8.3 percentage points; In the field of anti money laundering monitoring, the model has reduced the false positive rate of suspicious transactions from 28.7% to 6.3%, and reduced compliance labor costs by 4.2 million yuan per year. The study further validated the effectiveness of the model in handling credit imbalance data, improving classification accuracy to 91.5% through the Stacking algorithm and maintaining prediction stability of over 85% in stress tests. The practical application shows that the model not only improves the accuracy and efficiency of risk identification, but also provides a traceable and interpretable analysis path for decision makers through the visual feature map and dynamic risk scoring system, effectively balancing the technical progressiveness and regulatory transparency requirements.

This study provides a complete theoretical framework and technological implementation path for the intelligent transformation of the financial risk control field. Its innovative three-dimensional architecture design, multimodal data fusion strategy, and dynamic risk assessment mechanism have important practical value for building a more robust intelligent risk control ecosystem. Future research will further explore the application of models in sub fields such as high-frequency trading warning and cross-border fund flow monitoring, while combining causal reasoning frameworks to enhance model interpretability and promote the development of intelligent risk control technology towards higher efficiency and transparency.