Credit Risk Measurement, Decision Analysis, Transformation and Upgrading for Financial Big Data. (2nd May 2022)
- Record Type:
- Journal Article
- Title:
- Credit Risk Measurement, Decision Analysis, Transformation and Upgrading for Financial Big Data. (2nd May 2022)
- Main Title:
- Credit Risk Measurement, Decision Analysis, Transformation and Upgrading for Financial Big Data
- Authors:
- Wu, Wenshuai
- Other Names:
- Zhou Yu Academic Editor.
- Abstract:
- Abstract : There is no well-built theory on credit risk measurement and decision analysis for financial big data, and an effective and scientific evaluation system for them has not been formed. A review of them can contribute to grasping the abovementioned topics, understanding current issues, analyzing research problems, mastering research challenges, and predicting future research directions. Besides, this paper points out four research directions of credit risk measurement and decision analysis for financial big data. Moreover, this paper can provide some guidance directions and insights for practitioners, researchers, financial institutions, and government departments who have an interest in complex decision-making in big data.
- Is Part Of:
- Complexity. Volume 2022(2022)
- Journal:
- Complexity
- Issue:
- Volume 2022(2022)
- Issue Display:
- Volume 2022, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 2022
- Issue:
- 2022
- Issue Sort Value:
- 2022-2022-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-05-02
- Subjects:
- Chaotic behavior in systems -- Periodicals
Complexity (Philosophy) -- Periodicals
003 - Journal URLs:
- https://onlinelibrary.wiley.com/journal/10990526 ↗
http://onlinelibrary.wiley.com/ ↗
https://www.hindawi.com/journals/complexity/ ↗ - DOI:
- 10.1155/2022/8942773 ↗
- Languages:
- English
- ISSNs:
- 1076-2787
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3364.585500
British Library HMNTS - ELD Digital store - Ingest File:
- 21588.xml