RETRACTED ARTICLE: Financial risk assessment to improve the accuracy of financial prediction in the internet financial industry using data analytics models

Guansan Du () and Frank Elston ()
Additional contact information
Guansan Du: University of Southern Queensland
Frank Elston: Liaoning University

Operations Management Research, 2022, vol. 15, issue 3, No 20, 925-940

Abstract: Abstract A sound credit assessment mechanism has been explored for many years and is the key to internet finance development, and scholars divide credit assessment mechanisms into linear assessment and nonlinear assessment. The purpose is to explore the role of two important data analytics models including machine learning and deep learning in internet credit risk assessment and improve the accuracy of financial prediction. First, the problems in the current internet financial risk assessment are understood, and data of MSE (Micro small Enterprises) are chosen for analysis. Then, a feature extraction method based on machine learning is proposed to solve data redundancy and interference in enterprise credit risk assessment. Finally, to solve the data imbalance problem in the credit risk assessment system, a credit risk assessment system based on the deep learning DL algorithm is introduced, and the proposed credit risk assessment system is verified through a fusion algorithm in different models with specific enterprise data. The results show that the credit risk assessment model based on the machine learning algorithm optimizes the standard algorithm through the global optimal solution. The credit risk assessment model based on deep learning can effectively solve imbalanced data. The algorithm generalization is improved through layer-by-layer learning. Comparison analysis shows that the accuracy of the proposed fusion algorithm is 25% higher than that of the latest CNN (Convolutional Neural Network) algorithm. The results can provide a new research idea for the assessment of internet financial risk, which has important reference value for preventing financial systemic risk.

Keywords: Machine learning; Deep learning; Internet finance; Risk assessment management; Credit risk assessment (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s12063-022-00293-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:opmare:v:15:y:2022:i:3:d:10.1007_s12063-022-00293-5

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/12063

DOI: 10.1007/s12063-022-00293-5

Access Statistics for this article

Operations Management Research is currently edited by Jan Olhager and Scott Shafer

More articles in Operations Management Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().