Design of intelligent financial data management system based on higher-order hybrid clustering algorithm
Open accessAbstract
Amid the ever-expanding landscape of financial data, the importance of predicting potential risks through artificial intelligence methodologies has steadily risen. To achieve prudent financial data management, this manuscript delves into the domain of intelligent financial risk forecasting within the scope of system design. It presents a data model based on the variational encoder (VAE) enhanced with an attention mechanism meticulously tailored for forecasting a company’s financial peril. The framework called the ATT-VAE embarks on its journey by encoding and enhancing multidimensional data through VAE. It then employs the attention mechanism to enrich the outputs of the VAE network, thereby demonstrating the apex of the model’s clustering capabilities. In the experimentation, we implemented the model to a battery of training tests using diverse public datasets with multimodal features like AWA and CUB and verified with the local finance dataset. The results conspicuously highlight the model’s commendable performance in comparison to publicly available datasets, surpassing numerous deep clustering networks at this juncture. In the realm of financial data, the ATT-VAE model, as presented within this treatise, achieves a clustering accuracy index exceeding 0.7, a feat demonstrably superior to its counterparts in the realm of deep clustering networks. The method outlined herein provides algorithmic foundations and serves as a pivotal reference for the prospective domain of intelligent financial data governance and scrutiny.
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Deep learning shows substantial advantages in handling complex data modalities, particularly in medical imaging and time-series analysis.
“DL shows substantial advantages in handling complex data modalities [164, 165], particularly in medical imaging and time-series analysis.”
From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [164]
The Introduction says deep learning is suited to complex data modalities and that CNNs and LSTMs excel at processing time-series data. The Methodology section discusses computer-vision tasks, but neither passage establishes the claim specifically about medical imaging. (LLM, conf=0.88)
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Xie, X., Wu, C., Huang, Z., Zhou, Y., Huang, J., Dao, F., Yan, D., Deng, K., Lyu, H., Ma, C., et al. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag083 · Cited by 4 · see citing works
“DL shows substantial advantages in handling complex data modalities [164, 165], particularly in medical imaging and time-series analysis.”
- Authors
- Huang, L., & Lu, H.
- Affiliations
- Wuhan Technology and Business University (CN)
- Henan Institute of Economics and Trade (CN)
- Corresponding author
- Lu, Haitao
- ORCID
- Lu, Haitao
- Venue
- PeerJ Computer Science
- Publisher
- PeerJ
- Published
- 2024-01-24
- Volume
- 10
- Pages
- e1799
- ISSN
- 2376-5992 (ISSN-L)
- PMC
- PMC11636689
- OpenAlex
- W4391171420
- Cited by
- 10
- References
- 1
- Citations by year
- 2026: 5 · 2025: 2 · 2024: 3
- FWCI
- 9.94
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PeerJ Computer Science (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Business, Management and Accounting › Accounting › Financial Distress and Bankruptcy Prediction
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- financial risk prediction, deep clustering, variational autoencoder, attention mechanism, financial data management
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