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Designing an Intelligent Scoring System for Crediting Manufacturers and Importers of Goods in Industry 4.0

Author

Listed:
  • Mohsin Ali

    (Department of Computer Engineering, International Information Technology University, Almaty 050000, Kazakhstan)

  • Abdul Razaque

    (Department of Cybersecurity, Information Processing and Storage, Satbayev University, Almaty 050000, Kazakhstan)

  • Joon Yoo

    (School of Computing, Gachon University South Korea, Seongnam-si 13120, Republic of Korea)

  • Uskenbayeva Raissa Kabievna

    (Department of Software Engineering, Satbayev University, Almaty 050000, Kazakhstan)

  • Aiman Moldagulova

    (Department of Software Engineering, Satbayev University, Almaty 050000, Kazakhstan)

  • Satybaldiyeva Ryskhan

    (Department of Cybersecurity, Information Processing and Storage, Satbayev University, Almaty 050000, Kazakhstan)

  • Kalpeyeva Zhuldyz

    (Department of Software Engineering, Satbayev University, Almaty 050000, Kazakhstan)

  • Aizhan Kassymova

    (Department of Software Engineering, Satbayev University, Almaty 050000, Kazakhstan)

Abstract

Background : The modern credit card system is critical, but it has not been fully examined to meet the unique financial needs of a constantly changing number of manufacturers and importers. Methods : An intelligent credit card system integrates the features of artificial intelligence and blockchain technology. The decentralized and unchangeable ledger of the Blockchain technology significantly reduces the risk of fraud while maintaining real-time transaction recording. On the other hand, the capabilities of AI-driven credit assessment algorithms enable more precise, effective, and customized credit choices that are specifically tailored to meet the unique financial profiles of manufacturers and importers. Results : Several metrics, including predictive credit risk, fraud detection, credit assessment accuracy, default rate comparison, loan approval rate comparison, and other important metrics affecting the credit card system, have been investigated to determine the effectiveness of modern credit card systems when using Blockchain technology and AI. Conclusion : The study of developing an intelligent scoring system for crediting manufacturers and importers of goods in Industry 4.0 can be enhanced by incorporating user adoption. The changing legislation and increasing security threats necessitate ongoing monitoring. Scalability difficulties can be handled by detailed planning that focuses on integration, data migration, and change management. The research may potentially increase operational efficiency in the manufacturing and importing industries.

Suggested Citation

  • Mohsin Ali & Abdul Razaque & Joon Yoo & Uskenbayeva Raissa Kabievna & Aiman Moldagulova & Satybaldiyeva Ryskhan & Kalpeyeva Zhuldyz & Aizhan Kassymova, 2024. "Designing an Intelligent Scoring System for Crediting Manufacturers and Importers of Goods in Industry 4.0," Logistics, MDPI, vol. 8(1), pages 1-30, March.
    • Handle: RePEc:gam:jlogis:v:8:y:2024:i:1:p:33-:d:1360081
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    References listed on IDEAS

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    1. Niklas Bussmann & Paolo Giudici & Dimitri Marinelli & Jochen Papenbrock, 2021. "Explainable Machine Learning in Credit Risk Management," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 203-216, January.
    2. Purnima Rao & Satish Kumar & Meena Chavan & Weng Marc Lim, 2023. "A systematic literature review on SME financing: Trends and future directions," Journal of Small Business Management, Taylor & Francis Journals, vol. 61(3), pages 1247-1277, May.
    3. Chen, Yan & Bellavitis, Cristiano, 2020. "Blockchain disruption and decentralized finance: The rise of decentralized business models," Journal of Business Venturing Insights, Elsevier, vol. 13(C).
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