Enterprise AI Analysis
Financial Text Analysis Using 1D-CNN: Risk Classification and Auditing Support
This paper presents a novel financial text analysis method leveraging a 1D-CNN to overcome the limitations of traditional approaches in efficiency and accuracy for key information extraction and risk classification. The model demonstrates superior performance in extraction rate, coverage, and redundancy, and shows high accuracy and robustness even under noise. It offers a powerful tool for intelligent financial management and auditing.
Quantified Impact of Our Approach
Our proprietary 1D-CNN model delivers measurable improvements in financial text analysis accuracy and efficiency.
89.4%
Accuracy (Ours)
88.3%
Precision (Ours)
87.1%
Recall (Ours)
8.5%
Redundancy Rate (Ours)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
87.3%
Key Information Extraction Rate
The 1D-CNN model achieved an 87.3% extraction rate, outperforming traditional and other deep learning models. This highlights its efficiency in identifying critical semantic units from complex financial texts.
| Model | Extraction Rate (%) | Redundancy Rate (%) | Coverage Rate (%) |
|---|---|---|---|
| LR | 68.2 | 21.5 | 63.7 |
| SVM | 72.4 | 18.7 | 68.9 |
| LSTM | 78.9 | 12.3 | 75.4 |
| BILSTM | 82.7 | 10.8 | 80.1 |
| Ours (1D-CNN) | 87.3 | 8.5 | 84.9 |
The proposed 1D-CNN model significantly outperforms all comparative models in key information extraction, demonstrating higher efficiency and lower redundancy. Its superior performance across these metrics establishes it as an effective solution for financial text analysis.
1D-CNN Financial Text Analysis Process Flow
Financial Text Input (10-K Reports)
→
Word Embedding & Vectorization
→
1D Convolutional Layers (Local Feature Extraction)
→
Pooling Layers (Feature Dimension Reduction)
→
Multi-Layer Architecture (Hierarchical Feature Rep.)
→
Fully Connected Layer (Classification/Regression)
→
Risk Classification & Key Info Extraction Output
Application in Financial Auditing Support
The 1D-CNN model can significantly enhance financial auditing processes. For example, by efficiently mining key semantic units such as risk factors and financial anomalies from vast financial texts, auditors can quickly detect potential risks in financial statements, identify abnormal transactions, or uncover hidden information. This provides strong decision-making support, reduces manual effort, and improves the overall efficiency and accuracy of audits.
Calculate Your Potential ROI
Quantify the efficiency gains and cost savings your enterprise could achieve with AI-powered financial text analysis.
Estimated Annual Savings
$0
Hours Reclaimed Annually
0
Your AI Implementation Roadmap
A structured approach to integrating 1D-CNN into your financial operations for maximum impact.
Phase 1: Discovery & Strategy
In-depth analysis of existing financial text processes, identification of key challenges, and customization of the 1D-CNN model architecture to align with your specific enterprise needs and objectives.
Phase 2: Data Preparation & Training
Assistance with data acquisition from SEC Edgar, comprehensive data cleaning, annotation, and model training using your specific financial reports to ensure optimal performance and accuracy.
Phase 3: Integration & Deployment
Seamless integration of the trained 1D-CNN model into your existing financial management and auditing systems, followed by rigorous testing and validation in a live environment.
Phase 4: Optimization & Support
Continuous monitoring of model performance, ongoing optimization for evolving data patterns and regulatory changes, and dedicated technical support to ensure long-term value and operational excellence.
Conclusion
This paper introduced a financial text analysis model based on 1D-CNN, and systematically validated its effectiveness on the key information extraction and risk classification tasks. The experimental results demonstrate that the model effectively processes complex semantic features of financial texts and has better key indicators (extraction rate, redundancy rate, coverage) than traditional methods and other deep learning models. Furthermore, through the test of noisy data, the model exhibits a certain robustness, but it also reflects that the model in the high noise environment still has a lot of room for improvement in performance. Those results demonstrate the high practical value of applying 1D-CNN to the financial field. Its convolutional structure can effectively capture local patterns in texts, and multi-layer feature extraction realizes efficient semantic information processing. Applying the model in key tasks also verifies its potential for multi-scenario applications, including financial risk warning, automated auditing, and financial compliance detection. The findings herein provide a theoretical basis for promoting financial intelligentization and automization. However, this study is also limited in several aspects, it provides a direction for improvement in future research, as it is still has the problem of performance degradation under difficult semantic variants and high-noise environments. Future work may explore the adoption of stronger language models as pre-trained networks for increased model expressivity. Meanwhile, optimizing the model structure and training strategy is further expected to enhance its robustness and generalization ability in practical appli-cations. In summary, this study proposes novel ideas for financial text research and lays a theoretical foundation for future intelligent financial management and audit.
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