Deep Learning-Based Adaptive Online Intelligent Framework for a Blockchain Application in Risk Control of Asset Securitization

Liuyang Zhao, Yezhou Sha, Kaiwen Zhang and Jiaxin Yang
Additional contact information
Liuyang Zhao: Changchun University of Finance and Economics, China
Yezhou Sha: University of Nottingham, China
Kaiwen Zhang: University of Economics and Business, China
Jiaxin Yang: Southwestern University of Finance and Economics, China

International Journal of Data Warehousing and Mining (IJDWM), 2023, vol. 19, issue 4, 1-21

Abstract: Blockchain and distributed ledger technologies have attracted massive attention from both legal communities and businesses. Asset securitization is the procedure in which an issuer designs a financial instrument that is marketable by combining or merging different financial assets into one group. However, most securitization occurs with loans and other assets that generate receivables, such as consumer or business debt of various types. This article discusses the possible benefits of blockchain during the securitization process using the deep learning-based adaptive online intelligent framework (DLAOIF). The benefits can be significant, from reduced costs, time, and fraud risks to increased safety, trust, and accuracy. Tracking financial assets on a blockchain can reduce dependence on credit rating organizations and allow investors to monitor asset performance and the associated risk more carefully. It should improve investor confidence and increase secondary market interest.

Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJDWM.323182 (application/pdf)

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:igg:jdwm00:v:19:y:2023:i:4:p:1-21

Access Statistics for this article

International Journal of Data Warehousing and Mining (IJDWM) is currently edited by Eric Pardede

More articles in International Journal of Data Warehousing and Mining (IJDWM) from IGI Global
Bibliographic data for series maintained by Journal Editor ().