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Risk Measurement of the Financial Credit Industry Driven by Data: Based on DAE-LSTM Deep Learning Algorithm

Risk Measurement of the Financial Credit Industry Driven by Data: Based on DAE-LSTM Deep Learning Algorithm

Guizhi Li, Xuebiao Wang, Datian Bi, Jiayu Hou
Copyright: © 2022 |Volume: 30 |Issue: 11 |Pages: 20
ISSN: 1062-7375|EISSN: 1533-7995|EISBN13: 9781668464434|DOI: 10.4018/JGIM.308806
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MLA

Li, Guizhi, et al. "Risk Measurement of the Financial Credit Industry Driven by Data: Based on DAE-LSTM Deep Learning Algorithm." JGIM vol.30, no.11 2022: pp.1-20. http://doi.org/10.4018/JGIM.308806

APA

Li, G., Wang, X., Bi, D., & Hou, J. (2022). Risk Measurement of the Financial Credit Industry Driven by Data: Based on DAE-LSTM Deep Learning Algorithm. Journal of Global Information Management (JGIM), 30(11), 1-20. http://doi.org/10.4018/JGIM.308806

Chicago

Li, Guizhi, et al. "Risk Measurement of the Financial Credit Industry Driven by Data: Based on DAE-LSTM Deep Learning Algorithm," Journal of Global Information Management (JGIM) 30, no.11: 1-20. http://doi.org/10.4018/JGIM.308806

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Abstract

The risk measurement of financial credit industry is an important research issue in the field of financial risk assessment. The design of financial credit risk measurement algorithm can help investors avoid greater risks and obtain higher returns, so as to promote the benign development of financial credit industry. Based on the combined deep learning algorithm, this paper studies the risk measurement of financial and credit industry, and proposes a fusion algorithm of deep auto-encoder (DAE) and Long Short-Term Memory (LSTM) network. The algorithm recombines the value of fixed features by using the unsupervised mechanism of DAE, and extracts non fixed features for measurement combined with the memory characteristics of LSTM network. The experimental results show that: compared with single generalized regression neural network and LSTM network, the average accuracy of DAE-LSTM algorithm is improved by about 6.49% and 3.25% respectively, which has a better application effect in credit risk measurement.