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Artificial Intelligence Based Commercial Risk Management Framework for SMEs

Author

Listed:
  • Gerda Žigienė

    (School of Economics and Business, Kaunas University of Technology, Gedimino str. 50, LT-44239 Kaunas, Lithuania)

  • Egidijus Rybakovas

    (School of Economics and Business, Kaunas University of Technology, Gedimino str. 50, LT-44239 Kaunas, Lithuania)

  • Robertas Alzbutas

    (Faculty of Mathematics and Natural Sciences, Kaunas University of Technology, Studentų str. 50–218, LT-44239 Kaunas, Lithuania)

Abstract

Risk management in commercial processes is among the most important procedures affecting the competitiveness of small and medium-sized enterprises (SMEs), their innovativeness and potential contribution to global sustainable development goals (SDGs). The ecosystem of commercial processes is the prerequisite to manage risk faced by SMEs. Commercial risk assessment and management using elements of artificial intelligence, big data, and machine learning technologies could be developed and maintained as external services for a group of SMEs allowing to share costs and benefits. This paper aims to provide a conceptual framework of commercial risk assessment and management solution based on elements of artificial intelligence. This conceptualization is done on the background of scientific literature, policy documents, and risk management standards. Main building blocks of the framework in terms of commercial risk categories, data sources and workflow phases are presented in the article. Business companies, state policy, and academic research focused recommendations on the further development of the framework and its implementation are elaborated.

Suggested Citation

  • Gerda Žigienė & Egidijus Rybakovas & Robertas Alzbutas, 2019. "Artificial Intelligence Based Commercial Risk Management Framework for SMEs," Sustainability, MDPI, vol. 11(16), pages 1-23, August.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:16:p:4501-:d:259177
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    Cited by:

    1. Mohammad Rashed Hasan Polas & Valliappan Raju, 2021. "Technology and Entrepreneurial Marketing Decisions During COVID-19," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 22(2), pages 95-112, June.
    2. Gerda Zigiene & Egidijus Rybakovas & Rimgaile Vaitkiene, 2020. "Challenges in Applying Artificial Intelligence for Supply Chain Risk Management," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(4), pages 299-318.
    3. Di Vaio, Assunta & Palladino, Rosa & Hassan, Rohail & Escobar, Octavio, 2020. "Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review," Journal of Business Research, Elsevier, vol. 121(C), pages 283-314.
    4. Gerda Žigienė & Egidijus Rybakovas & Rimgailė Vaitkienė & Vaidas Gaidelys, 2022. "Setting the Grounds for the Transition from Business Analytics to Artificial Intelligence in Solving Supply Chain Risk," Sustainability, MDPI, vol. 14(19), pages 1-23, September.
    5. Lilian Mboya, 2021. "Augmenting Human Minds: Artificial Intelligence and Big Data in Financial Risk Assessment," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 5(09), pages 307-311, September.

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