Design of intelligent financial data management system based on higher-order hybrid clustering algorithm
1School of Management, Wuhan Technology And Business University, Wuhan, China.
Peerj. Computer Science
|December 13, 2024
Summary
This study introduces ATT-VAE, an AI model for financial risk forecasting. It enhances variational autoencoders with attention mechanisms, improving financial risk prediction and data governance.
Area of Science:
- Artificial Intelligence
- Financial Data Science
- Machine Learning
Background:
- The increasing volume of financial data necessitates advanced methods for risk prediction.
- Current financial risk management requires intelligent systems for prudent data handling.
- System design for financial risk forecasting is a critical area of research.
Purpose of the Study:
- To develop and present an intelligent financial risk forecasting model.
- To design a system leveraging AI for predicting company financial peril.
- To enhance financial data management through advanced AI techniques.
Main Methods:
- A novel data model, ATT-VAE, based on variational autoencoder (VAE) with an attention mechanism.
- Encoding and enhancing multidimensional financial data using VAE.
- Employing an attention mechanism to enrich VAE outputs for improved clustering.
Main Results:
- The ATT-VAE model demonstrated superior performance on diverse public and local financial datasets.
- Achieved a clustering accuracy index exceeding 0.7, outperforming existing deep clustering networks.
- Verified effectiveness using multimodal datasets (AWA, CUB) and local finance data.
Conclusions:
- The ATT-VAE model offers a robust framework for intelligent financial risk forecasting.
- The proposed method provides algorithmic foundations for future financial data governance.
- This research serves as a pivotal reference for AI-driven financial scrutiny.
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