Advanced risk management models for supply chain finance

Advanced risk management models for supply chain finance

Received on 02 April 2024; revised on 07 May 2024; accepted on 10 May 2024 | Uzoma Okwudili Nnaji, Lucky Bamidele Benjamin, Nsisong Louis Eyo-Udo, Emmanuel Augustine Etukudoh
This review paper explores the transformative potential of advanced risk management models in enhancing the resilience and efficiency of supply chain finance (SCF). The paper examines the application and development of Artificial Intelligence (AI), Machine Learning (ML), Big Data analytics, and blockchain technology, highlighting their role in transitioning from traditional reactive strategies to proactive and predictive risk management approaches. Despite the promising advantages, the paper also addresses significant implementation challenges, model limitations, and regulatory and ethical considerations. Recommendations for effective deployment and areas for future research are discussed, aiming to guide academics, industry professionals, and policymakers in harnessing advanced risk management models for a more robust SCF ecosystem. The paper covers theoretical frameworks, recent advances, and the development of advanced models, comparing them with traditional models. It also discusses the challenges and limitations of implementing these models, including technological infrastructure, data privacy, model obsolescence, and ethical considerations. The conclusion emphasizes the need for continuous innovation, collaboration, and research to address these challenges and realize the full potential of advanced risk management models in SCF.This review paper explores the transformative potential of advanced risk management models in enhancing the resilience and efficiency of supply chain finance (SCF). The paper examines the application and development of Artificial Intelligence (AI), Machine Learning (ML), Big Data analytics, and blockchain technology, highlighting their role in transitioning from traditional reactive strategies to proactive and predictive risk management approaches. Despite the promising advantages, the paper also addresses significant implementation challenges, model limitations, and regulatory and ethical considerations. Recommendations for effective deployment and areas for future research are discussed, aiming to guide academics, industry professionals, and policymakers in harnessing advanced risk management models for a more robust SCF ecosystem. The paper covers theoretical frameworks, recent advances, and the development of advanced models, comparing them with traditional models. It also discusses the challenges and limitations of implementing these models, including technological infrastructure, data privacy, model obsolescence, and ethical considerations. The conclusion emphasizes the need for continuous innovation, collaboration, and research to address these challenges and realize the full potential of advanced risk management models in SCF.
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