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Advanced Hybrid Information Processing. 6th EAI International Conference, ADHIP 2022, Changsha, China, September 29-30, 2022, Proceedings, Part I

Research Article

Research on Operational Risk Monitoring Method of Intelligent Financial System Based on Deep Learning and Improved RPA

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-28787-9_42,
        author={Liang Yuan and Hui Zhu},
        title={Research on Operational Risk Monitoring Method of Intelligent Financial System Based on Deep Learning and Improved RPA},
        proceedings={Advanced Hybrid Information Processing. 6th EAI International Conference, ADHIP 2022, Changsha, China, September 29-30, 2022, Proceedings, Part I},
        proceedings_a={ADHIP},
        year={2023},
        month={3},
        keywords={Deep learning Improve RPA Intelligent financial system Operational risk monitoring Optimization algorithm},
        doi={10.1007/978-3-031-28787-9_42}
    }
    
  • Liang Yuan
    Hui Zhu
    Year: 2023
    Research on Operational Risk Monitoring Method of Intelligent Financial System Based on Deep Learning and Improved RPA
    ADHIP
    Springer
    DOI: 10.1007/978-3-031-28787-9_42
Liang Yuan1,*, Hui Zhu1
  • 1: State Grid Huitong Jincai (Beijing) Information Technology Co., Ltd.
*Contact email: lihongsheng51@163.com

Abstract

The accuracy of traditional financial system operational risk monitoring is low. Therefore, this paper proposes a method of intelligent financial system operational risk monitoring based on deep learning and improved RPA. Set the financial monitoring index and obtain the warning threshold parameters; Using deep neural network method to mine key risk indicators and obtain reconstruction coefficients of data mining errors of financial system; By improving the RPA method to calculate the fit degree of financial risk, it matches the internal business process of the enterprise; The operational risk monitoring algorithm of financial system is designed to realize the operational risk monitoring of financial system. The experimental results show that the risk monitoring accuracy of the design method is 80.3%, and the overall test threshold of the model is 0.5 after the introduction of non-financial indicators, which shows that it can be applied in practice.

Keywords
Deep learning Improve RPA Intelligent financial system Operational risk monitoring Optimization algorithm
Published
2023-03-22
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-28787-9_42
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