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DL-FinRisk

Qikun Ao et al.

Journal of Organizational and End User Computing2026https://doi.org/10.4018/joeuc.406095article
AJG 1ABDC B
Weight
0.50

Abstract

To address existing financial risk assessment models' poor adaptability to complex scenarios, weak multi-source data integration, and inadequate dynamic threat response, this study proposes DL-FinRisk, a deep learning-driven framework incorporating Data Law-oriented compliance considerations. It integrates multi-modal fusion for heterogeneous data unification, a ResNet-LSTM hybrid architecture for spatial-temporal feature extraction, Bayesian network-based Dynamic Security Assessment (DSA) for real-time risk updates, and TOPSIS for decision-making. Validated on real-world financial data and public datasets under Data Law and regulatory compliance constraints, DL-FinRisk achieves 95.7% accuracy, 94.0% F1-score, and 73.1% Risk Reduction Rate, outperforming baseline models.

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https://doi.org/https://doi.org/10.4018/joeuc.406095

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@article{qikun2026,
  title        = {{DL-FinRisk}},
  author       = {Qikun Ao et al.},
  journal      = {Journal of Organizational and End User Computing},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.4018/joeuc.406095},
}

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DL-FinRisk

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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