Risk Measurement of the Financial Credit Industry Driven by Data

Autor:	Guizhi Li, Xuebiao Wang, Datian Bi, Jiayu Hou
Rok vydání:	2022
Předmět:	Information Systems and Management Strategy and Management Management Science and Operations Research Business and International Management Computer Science Applications
Zdroj:	Journal of Global Information Management. 30:1-20
ISSN:	1533-7995 1062-7375
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::b66fa16caf8735fecd55661d70eb9de6 https://doi.org/10.4018/jgim.308806 Zobrazit plný text záznamu