This study examines the role of financial service providers (FSPs) in assessing the supply chain credit of small and medium-sized enterprises (SMEs) and how they help SMEs obtain supply chain finance (SCF) through an established digital platform using big data analytics (BDA).,This study conducted data mining analysis on the archival data of China's FSPs in the mobile production industry from 2015 to 2018, using neural networks in the first stage and multiple regression in the second stage.,The findings suggest that digital platforms sponsored by FSPs have a discriminative effect based on implicit BDA on identifying the quality and potential risks of borrowers. The results also show that tailored information utilised by FSPs has a supportive effect based on explicit BDA in helping SMEs obtain financing.,This study contributes to the emergent research on BDA in supply chain management by extending the contextual research on information signalling and platform theory in SCF. Furthermore, it examines the distinctive financing decision models of FSPs and provides a solution that addresses the information deficiency and overload of both lenders and borrowers and plays a certain reference role in alleviating the financing problems of SMEs.

Big data analytics in digital platforms: how do financial service providers customise supply chain finance?

• The paper uses both enterprise demographic data and financing behavioral data for credit risk prediction of SMEs in SCF. • The paper proposes the DeepRisk approach by using multi-modal deep learning to fuse the two different sources of data. • Experiments on SCF dataset demonstrate that the DeepRisk approach outperforms the baseline methods in terms of precision, recall, F1-score, AUC and economic loss. • The fusion of the two different sources of data is superior to the existing studies on credit risk prediction of SMEs in SCF. The credit risk of small and medium-sized enterprises (SMEs) in supply chain finance (SCF) is defined as the probability that the SME would default on loans derived from financing for the SCF platform. Traditional models make use of merely the static data of SMEs, such as enterprise demographic data and financial statement data, to predict the credit risk of SMEs in SCF. Nevertheless, behavioral data, which reflect the dynamic financing behavior of SMEs in SCF, are overlooked by these models, which limits the performance of credit risk prediction. To address this problem, a novel approach is proposed called DeepRisk to fuse enterprise demographic data and financing behavioral data to predict the credit risk of SMEs in SCF. We adopt the multi-modal learning strategy to fuse the two different sources of data. The concatenated vectors derived from data fusion are then used as the input of the feed forward neural network to predict the credit risk of SMEs. Experiments on a real SCF dataset demonstrate that the proposed DeepRisk approach outperforms the baseline methods in credit risk prediction in terms of precision, recall, F1-score, area under curve (AUC), and economic loss. The fusion of the two different sources of data is superior to the existing approaches to the credit risk prediction of SMEs in SCF. Both the static enterprise demographic data and the dynamic financing behavioral data are crucial to improve the credit risk prediction of SMEs. Nevertheless, the variables derived from the financing behavioral data have a better predictability than those from the enterprise demographic data. Managerial implications have been identified for decision makers involved in SCF in utilizing the benefits of SCF and in managing their credit risks. 

Credit risk prediction of SMEs in supply chain finance by fusing demographic and behavioral data

Operations strategy for supply chain finance with asset-backed securitization: Centralization and blockchain adoption


Purpose
Agriculture and cultivation firms are facing severe competition in the saturated market. Due to the characteristics of heavy assets, low investment return, long cycle and high price fluctuation, agri-food firms require innovations for capital support. The purpose of this paper is to provide valuable insights on how firms in the food/agricultural industry approach innovations and reinforce their advantages through functional and structural innovations by adopting supply chain finance (SCF).


Design/methodology/approach
This research adopts a single-case study methodology to investigate the innovations and mechanisms taking place at H Corp Agriculture Group (H Corp hereafter), a Chinese egg company.


Findings
The findings of this paper indicate that SCF could have a great impact on supply chain management through functional and structural innovations throughout the supply chain and solve the capital constraint problems in the agricultural development process, promoting the implementation of the integration strategy as well as innovation in the agricultural industry chain. The research also shows that supply chain structural and functional innovations could promote corporate social responsibility (CSR) and creating shared value (CSV).


Research limitations/implications
The research contributes to the application of SCF mechanisms and the realization of CSV and CSR jointly – both in the literature and in firms’ practices. It also contributes to the extension of structural and functional innovations and vertical integration of the supply chain. However, generalizability and universality are insufficient for a single case study in the specified industry. Data collection and quantitative analysis could be extended for further research.


Originality/value
The study addresses the need for comprehensive research on SCF and its applications. It proposes effective and efficient strategies for agri-food firms applying SCF to overcome industry capital constraints and develop competitiveness. It also provides a balanced and positive circulation between economic value and social value, realizing CSR and CSV.


The impact of supply chain finance on corporate social responsibility and creating shared value: a case from the emerging economy

The 2019 novel coronavirus disease (COVID-19) pandemic has significantly impacted several aspects of the society and the economy. A problem that needs prompt attention in this situation is the increasing difficulties faced by small- and medium-sized enterprises (SMEs) in raising capital, which has aroused great concern from multiple stakeholders such as public administrations and regulators. As the major supply of capital, financial service providers (FSPs) play a critical role in financing SMEs. However, how FSPs deal with SME financing during shocks has not yet been fully researched. Accordingly, in this study, a theoretical framework based on expectancy theory is proposed to explore the expected strategic adjustments of FSPs in financing SMEs. Specifically, this study investigates 272 FSPs in China on their expectancy and attitude on financing to SMEs during the COVID-19 pandemic. Furthermore, this study has divided FSPs into three categories: commercial banks, non-bank financial institutions, and credit-enhanced FSPs. Differences among these categories are compared and analyzed.

/pdf/how-different-types-of-financial-service-providers-support-zfxm4llb3a.pdf

How different types of financial service providers support small- and medium- enterprises under the impact of COVID-19 pandemic: from the perspective of expectancy theory

How do supply chain network and SMEs’ operational capabilities enhance working capital financing? An integrative signaling view

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How do supply chain network and SMEs’ operational capabilities enhance working capital financing? An integrative signaling view