Work

Risk Measurement of the Financial Credit Industry Driven by Data

Year: 2022

Type: article

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.

Source: Journal of Global Information Management

DOI: 10.4018/jgim.308806

Authors: Guizhi Li, Xuebiao Wang, Datian Bi, Jiayu Hou

Institutions: Yingkou Institute of Technology, Dongbei University of Finance and Economics, Jilin University

Language: English

Topic: Financial Distress and Bankruptcy Prediction

Subfield: Accounting

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: diamond

Funder: National Social Science Fund of China

Award: G5175448345