Supply Chain Finance and Management

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Economics and Finance".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 972

Special Issue Editors


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Guest Editor
Bictevac Laboratory—Business Information and Communication Technologies in Value Chains Laboratory, Department of Agribusiness and Supply Chain Management, School of Applied Economics and Social Sciences, Agricultural University of Athens, 118 55 Athens, Greece
Interests: digital marketing; big data; web analytics; decentralized & centralized payment networks; Fintech; modeling & simulation; DSS
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Agribusiness and Supply Chain Management, School of Applied Economics and Social Sciences, Agricultural University of Athens, 11855 Athens, Greece
Interests: management information systems; DSS; simulation modelling; supply chain management

Special Issue Information

Dear Colleagues,

Innovations in fintech within the realm of supply chain finance and management are characterized by the integration of cutting-edge technology, efficiency, transparency, and collaboration across the supply chain. Key trends encompass the utilization of blockchain to ensure secure transactions, the application of artificial intelligence (AI) and machine learning (ML) for risk assessment, and the implementation of digital platforms for trade financing. Noteworthy developments include the rise in invoice financing and factoring platforms, contributing to the ongoing digitalization of supply chain processes. Financial services are now intricately woven into the fabric of the supply chain ecosystem, emphasizing collaborative efforts among fintech providers, suppliers, and buyers. Notable highlights include the following:

The increasing adoption of blockchain technology in supply chain finance enhances transaction transparency, security, and efficiency. Smart contracts play a pivotal role in automating processes and minimizing the reliance on intermediaries.

Fintech enterprises are actively creating digital platforms that interconnect buyers, suppliers, and financial institutions. These platforms streamline the exchange of financial information, invoices, and payment data, thereby enhancing the accessibility and efficiency of supply chain financing.

AI and ML algorithms are integrated into fintech solutions for risk assessment within supply chain finance. These technologies excel in predicting disruptions, evaluating creditworthiness, and optimizing financing decisions.

The overarching trend towards digitization in supply chain processes has a profound impact on supply chain finance. Fintech solutions seamlessly integrate with digital tools for procurement, inventory management, and logistics, optimizing financial processes within these digital ecosystems.

Fintech companies are exploring the incorporation of financial services directly into the supply chain ecosystem. Collaborative efforts among fintech providers, suppliers, and buyers result in integrated financial solutions being seamlessly woven into existing supply chain management processes.

Dr. Nikolaos T. Giannakopoulos
Dr. Dimitrios K. Nasiopoulos
Guest Editors

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Keywords

  • FinTech
  • supply chain
  • finance
  • administration
  • advertising
  • marketing
  • digital marketing
  • supply chain management
  • innovation
  • artificial intelligence (AI)
  • machine learning (ML)
  • management information systems (MIS)
  • crisis management

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Published Papers (1 paper)

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42 pages, 3269 KB  
Systematic Review
Artificial Intelligence in Disaster Supply Chain Risk Management: A Bibliometric Analysis with Financial Risk Implications
by Ioannis Dimitrios Kamperos, Nikolaos Giannakopoulos, Damianos Sakas and Niki Glaveli
J. Risk Financial Manag. 2026, 19(5), 310; https://doi.org/10.3390/jrfm19050310 - 25 Apr 2026
Viewed by 549
Abstract
Disruptions caused by disasters, pandemics, and systemic crises have increased the complexity and vulnerability of global supply chains, highlighting the need for advanced analytical approaches to risk and resilience management. In this context, artificial intelligence (AI) has emerged as a promising analytical capability [...] Read more.
Disruptions caused by disasters, pandemics, and systemic crises have increased the complexity and vulnerability of global supply chains, highlighting the need for advanced analytical approaches to risk and resilience management. In this context, artificial intelligence (AI) has emerged as a promising analytical capability for improving risk assessment and decision-making in disrupted supply chains. The study follows PRISMA 2020 reporting guidelines adapted for bibliometric research and presents a bibliometric and knowledge-mapping analysis of artificial intelligence applications in disaster supply chain risk and resilience management. Using the Web of Science Core Collection, a dataset of 288 peer-reviewed publications was analyzed through keyword co-occurrence, bibliographic coupling, citation analysis, and collaboration network mapping. The findings indicate a rapidly expanding research field in which AI supports predictive risk assessment, real-time monitoring, and resilience-oriented decision-making in disaster-prone supply networks. The analysis identifies dominant thematic clusters, emerging research directions, and opportunities for integrating AI-enabled analytics into supply chain risk management frameworks. The mapped literature also suggests secondary interpretive implications for financial risk exposure and supply chain finance, rather than indicating a separately operationalized finance-specific bibliometric subfield. To enhance interpretive depth, an AI-assisted analytical layer was applied to refine thematic clusters and detect emerging trends. However, this layer operates as a complementary interpretive tool and is subject to methodological limitations, including sensitivity to keyword semantics, dependence on bibliometric outputs, and potential interpretive bias in AI-assisted thematic labeling. Consequently, the AI-assisted analysis is used to support, rather than replace, bibliometric findings. Overall, this study contributes to the emerging literature on artificial intelligence in disaster supply chain risk management and highlights future research opportunities, including improved methodological integration and enhanced analytical transparency in AI-assisted bibliometric research. Full article
(This article belongs to the Special Issue Supply Chain Finance and Management)
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