The Optimization of Supply Chain Financing for Bank Green Credit Using Stackelberg Game Theory in Digital Economy Under Internet of Things

The Optimization of Supply Chain Financing for Bank Green Credit Using Stackelberg Game Theory in Digital Economy Under Internet of Things

Hui Zhang, Fengrui Zhang, Bing Gong, Xuan Zhang, Yifan Zhu
Copyright: © 2023 |Pages: 16
DOI: 10.4018/JOEUC.318474
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The aim is to improve small and medium-sized enterprises (SMEs)' core competitiveness and financing attainability using deep learning (DL) under economic globalization. Accordingly, this work constructs a supply chain symbiosis system based on DL, economics, and Stackelberg game theory following a status quo analysis of the financing status of SMEs. Afterward, a structural framework of supply chain financing (SCF) is designed. Further, it verifies the effectiveness of the proposed back propagation neural network (BPNN) credit evaluation model through specific enterprise data. The results show that the proposed internet of things (IoT)-based SCF SMEs-oriented BPNN credit evaluation model reaches a prediction accuracy of 91.4%. It effectively eliminates information asymmetry between banks and various capitals. As a result, banks can guarantee operation funds for the supply chain SMEs and help them minimize project risks by lowering financing leverage and through information transparency.
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By the end of the 20th century, market globalization and financial liberalization have become inevitable. The contradiction between the cost reduction of global core enterprises and the rising price of raw materials and human resources has become ever more severe. As a result, core enterprises with high market standing adopt credit sales mode and collection before delivery mode to reduce cash flow pressure and strengthen market competitiveness. In the credit sales mode, core enterprises pay after small- and medium-sized enterprises (SMEs) have provided raw materials. In the collection before delivery mode, core enterprises collect advanced payment from SMEs before delivering goods. However, both sales modes impose great finances on SMEs (Косимова, 2020; Ustyuzhanina et al., 2019). Commercial banks use supply chain financing (SCF) to optimize the traditional point-to-point service for supply chain core enterprises. In particular, SCF can connect the core enterprises with the upstream and downstream supply chain SMEs by linking the raw material supply, semi-finished product production, and user-end commodity delivery services. Thus, SCF forms a risk-sharing community by connecting the supply chain participants, suppliers, manufacturers, distributors, retailers, and end-users.

Meanwhile, SCF adopts a new whole supply chain-oriented credit evaluation mechanism over the original single enterprise-oriented credit evaluation mechanism. Because of the new credit evaluation mechanism, financial institutions can implement better financial services for multiple subjects in the supply chain. In simpler terms, the endorsement or guarantee by core enterprises in SCF helps weaken the qualification weight of SMEs in the credit review, effectively improving SMEs’ attainability of credit loans (Chen, 2019; Swanson, 2018; Raimo et al., 2021).

So far, foreign studies on SCF are relatively limited. This work summarizes them as the whole supply chain-oriented study, the SCF financing scheme-oriented analysis, SCF technical support-oriented research, and SCF third-party logistics-oriented exploration. Domestic researchers have investigated SCF mainly from the financing mode, financing risk, and financing credit risk perspectives. Thereby, both domestic and international researchers have made efforts from the theory and application of SCF. However, no universal SCF SMEs-oriented credit risk evaluation index system (EIS) is acknowledged in the academic circle (He et al., 2019; Wang et al., 2017). Thus, the index selection is mostly very subjective. In the past, quantitative indexes were chosen to evaluate SCF’s credit risk. In practice, scholars prefer fuzzy qualitative indexes to assess credit risk. Principal component analysis (PCA) or regression analysis can somehow trade off the impact of subjective evaluation. Nevertheless, they require a large-scale training set, which is difficult to operate (Song et al., 2020; Fu & Zhu, 2019).

According to the idea of SCF, this work attempts to transfer the bank’s focus from assessing the SMEs’ credit to evaluating the entire supply chain, from considering static indexes, such as financial data of SMEs, to considering dynamic transaction facts. This enables banks to grasp the essence behind the SMEs’ credit risks to deal with various threats, develop SCF services, and help SCF enterprises solve financing difficulties. Following an in-depth study and understanding of back propagation neural network (BPNN) characteristics and the advantages of BPNN-based credit risk evaluation for SCF enterprises, this work proposes the SCF SMEs-oriented credit risk evaluation model. It tries to minimize the influence of human factors and fuzzy randomness and ensures the objectivity and accuracy of the evaluation results. Moreover, BPNN has strong dynamics. With the increase of samples and the advance of time, it can further learn and track financial data dynamically.

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