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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Liuyang Zhao & Yezhou Sha & Kaiwen Zhang & Jiaxin Yang, 2023. "Deep Learning-Based Adaptive Online Intelligent Framework for a Blockchain Application in Risk Control of Asset Securitization," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 19(4), pages 1-21, April.
    • Handle: RePEc:igg:jdwm00:v:19:y:2023:i:4:p:1-21
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    References listed on IDEAS

    as
    1. Dusko Knezevic, 2018. "Impact of Blockchain Technology Platform in Changing the Financial Sector and Other Industries," Montenegrin Journal of Economics, Economic Laboratory for Transition Research (ELIT), vol. 14(1), pages 109-120.
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