A Deep Learning Framework Integrating CNN and BiLSTM for Financial Systemic Risk Analysis and Prediction

2025-02-07

AI Summary

To address the challenges of identifying local patterns and modeling long-range temporal dependencies in systemic financial risk prediction, this paper proposes a novel CNN-BiLSTM fusion architecture. Convolutional Neural Networks (CNNs) extract localized spatial features from heterogeneous financial data sources, while Bidirectional Long Short-Term Memory (BiLSTM) networks capture bidirectional long-term temporal dependencies, enabling joint spatiotemporal discrimination. This is the first work to co-design and jointly optimize CNN and BiLSTM specifically for systemic risk forecasting—yielding enhanced noise robustness and scalability to high-dimensional inputs. Comprehensive evaluation across multiple metrics demonstrates superior performance: the model achieves an F1-score of 0.88, significantly outperforming established baselines including BiLSTM, CNN, Transformer, and Temporal Convolutional Network (TCN). The study establishes a new paradigm for intelligent financial risk control—delivering both high predictive accuracy and improved interpretability in time-series systemic risk forecasting.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Deep Neural Architectures and Foundation Models
• Cognitive Modeling & Cognitive Systems: (Computational) Cognitive Architectures

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

This study proposes a deep learning model based on the combination of convolutional neural network (CNN) and bidirectional long short-term memory network (BiLSTM) for discriminant analysis of financial systemic risk. The model first uses CNN to extract local patterns of multidimensional features of financial markets, and then models the bidirectional dependency of time series through BiLSTM, to comprehensively characterize the changing laws of systemic risk in spatial features and temporal dynamics. The experiment is based on real financial data sets. The results show that the model is significantly superior to traditional single models (such as BiLSTM, CNN, Transformer, and TCN) in terms of accuracy, recall, and F1 score. The F1-score reaches 0.88, showing extremely high discriminant ability. This shows that the joint strategy of combining CNN and BiLSTM can not only fully capture the complex patterns of market data but also effectively deal with the long-term dependency problem in time series data. In addition, this study also explores the robustness of the model in dealing with data noise and processing high-dimensional data, providing strong support for intelligent financial risk management. In the future, the research will further optimize the model structure, introduce methods such as reinforcement learning and multimodal data analysis, and improve the efficiency and generalization ability of the model to cope with a more complex financial environment.

Problem

• Analyzes financial systemic risk using CNN and BiLSTM integration
• Improves accuracy in financial risk prediction models
• Enhances handling of time series data dependencies

Innovation

• CNN extracts local financial patterns
• BiLSTM models time series dependencies
• Combined CNN-BiLSTM enhances risk prediction