Financial service providers and banks’ role in helping SMEs to access finance

Hua, Song; Yu, Kumlan; Lu, Qiang

doi:10.1108/ijpdlm-11-2016-0315

Cited by 96 publications

(121 citation statements)

References 55 publications

Supporting

Mentioning

117

Contrasting

Order By: Relevance

“…Gelsomino et al (2016) and Hofmann (2005) emphasize that the FIs can improve their risk-assessment ability in estimating the probability of default, especially for SMEs using the SCF solution. Caniato et al (2016) and Song et al (2018) argue that SCF is an alternative method of overcoming the problem of information asymmetry, so it can control potential risks and provides easy credit to SMEs.…”

mentioning

confidence: 99%

“…In this paper, we adopt the same definition. Since SCF is different from the traditional financing approach, it has extensive interactions with related SMEs (Song et al, 2018, Stemmler, 2002. Thus, there are two main influencing factors-the "SMEs itself-oriented" factor and the "supply chain financeoriented" factor-that may result in SMEs' inability to fulfil their legal obligations and FIs facing SMEs' credit risk in SCF , Zhu et al, 2017.…”

mentioning

confidence: 99%

“…(1) The SMEs' credit risk influencing factors of SMEs that are self-oriented Scholars argue that information asymmetry is the root cause of SMEs' credit risk (Altman and Sabato, 2007, Altman et al, 2010, Chen et al, 2010, Li et al, 2016, Song and Zhang, 2017, Stiglitz and Weiss, 1981. To control SMEs' credit risks, FIs attempt to overcome the problem of information asymmetry of SMEs through the SCF solution (Caniato et al, 2016, Song et al, 2018. The SCF solution has extensive interactions with related SMEs compared with traditional financing solutions, acquires complete transaction information and business credit of every member of the supply chain, and adopts specific solutions (e.g., receivable transfers, closed-loop business, relational embeddedness, and a combination of outcome control and behavioural control) that significantly reduce the information asymmetry of SMEs (Caniato et al, 2016, Martin and Hofmann, 2017, Song et al, 2018.…”

mentioning

confidence: 99%

“…To control SMEs' credit risks, FIs attempt to overcome the problem of information asymmetry of SMEs through the SCF solution (Caniato et al, 2016, Song et al, 2018. The SCF solution has extensive interactions with related SMEs compared with traditional financing solutions, acquires complete transaction information and business credit of every member of the supply chain, and adopts specific solutions (e.g., receivable transfers, closed-loop business, relational embeddedness, and a combination of outcome control and behavioural control) that significantly reduce the information asymmetry of SMEs (Caniato et al, 2016, Martin and Hofmann, 2017, Song et al, 2018. However, FIs are not directly involved in SMEs' actual operations and production; therefore, to reduce possible the information asymmetry of SMEs, FIs take better care of SMEs' business counterparts (i.e., CEs) in the SCF solution (Song et al, 2018).…”

mentioning

confidence: 99%

See 3 more Smart Citations

Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach

You

Zhou

Xie

et al. 2019

International Journal of Production Economics

223

162

View full text Add to dashboard Cite

In recent years, financial institutions (FIs) have tentatively utilized supply chain finance (SCF) as a means of solving the financing issues of small and medium-sized enterprises (SMEs). Thus, forecasting SMEs' credit risk in SCF has become one of the most critical issues in financing decision-making. Nevertheless, traditional credit risk forecasting models cannot meet the needs of such forecasting. Many researchers argue that machine learning (ML) approaches are good tools. Here we propose an enhanced hybrid ensemble ML approach called RS-MultiBoosting by incorporating two classic ensemble ML approaches, random subspace (RS) andMultiBoosting, to improve the accuracy of forecasting SMEs' credit risk. The experimental samples, originating from data on forty-six quoted SMEs and seven quoted core enterprises (CEs) in the Chinese securities market between 31 March 2014 and 31 December 2015, are collected to test the feasibility and effectiveness of the RS-MultiBoosting approach. The forecasting result shows that RS-MultiBoosting has good performance in dealing with a small sample size. From the SCF perspective, the results suggest that to enhance SMEs' financing ability, 'traditional' factors, such as the current and quick ratio of SMEs, remain critical. Other SCF-specific factors, for instance, the features of trade goods and the CE's profit margin, play a significant role.

show abstract

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach

You

Zhou

Xie

et al. 2019

International Journal of Production Economics

223

162

View full text Add to dashboard Cite

show abstract

“…Consequently, the demand for capital from within the SC, e.g. from companies directly involved in supply chain finance (SCF) schemes or acting as financial service providers (FSPs) has increased [9][10][11][12][13][14][15][16]. For this reason, the importance of effective WCM has raised dramatically, especially for SCs from emerging markets, which faced difficulties with access to capital, limited financial infrastructure and legal, regulatory and accounting uncertainties in the first place.…”

Section: Introductionmentioning

confidence: 99%

Working Capital Optimization in Supply Chains

Zenkevich

Ivakina

2018

КФ

View full text Add to dashboard Cite

This article is devoted to working capital management and its optimization on an inter-organizational level when supply chain members operate collaboratively. We aim to develop and validate a model of collaborative approach to working capital management in supply chains for cases of constrained liquidity and imposed return requirements using supply chain finance (factoring, reverse factoring and inventory financing). As such, we suggest a tool of working capital optimization using financial terms and cash flows verified on Russian supply chain data. Mathematical modeling is suggested as a method to modify an existing working capital management model on the grounds of collaborative financial cost minimization under industry specific liquidity constraints. These liquidity constraints are constructed in such a way as to eliminate possible violations from companies, because their violation will lead to the inverse relation between liquidity and rate of return. The results of the optimization provide recommended values for cash conversion cycle elementsdays of inventories, days of accounts payable, days of accounts receivable -that guarantee the coordinating effect of collaborative working capital management. Calculation, further optimization and monitoring of cash conversion cycle values sustain effective working capital management on an inter-organizational level while meeting the liquidity and return levels for each company in a chain. The suggested model can be implemented for a day-to-day decision making process by companies oriented to stay competitive in the long run. Besides, the results obtained show the potential for further coordination among the key members of the supply chain in terms of aligning financial, product, and information flows. Despite the fact that the model provides a static solution to the problem of collaborative working capital management, it has potential for the further development of a dynamic algorithm. Future research should seek to investigate the possible imputation options for gained costs reduction values on the grounds of cooperative games with a coalition structure.

show abstract

Joint coordination contract for capital‐constrained supply chains under asymmetric information

2022

View full text Add to dashboard Cite

A two‐echelon supply chain considering capital constraints and asymmetric information is established. The supplier provides credit guarantees for the capital‐constrained retailer to loan from the bank, and its information about the retailer's capital type is asymmetric. The equilibrium decisions of supply chain participants are analyzed by using the Stackelberg game theory and principal–agent theory. Then, a joint contract is designed to eliminate the double marginalization effect. The results show that the supplier and the bank make different decisions for different types of retailers to avoid adverse selection. The joint contract of revenue‐sharing and transfer payment can coordinate the supply chain.

show abstract

Financial service providers and banks’ role in helping SMEs to access finance

Cited by 96 publications

References 55 publications

Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach

Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach

Working Capital Optimization in Supply Chains

Joint coordination contract for capital‐constrained supply chains under asymmetric information

Contact Info

Product

Resources

About