There is no second thought that financing is a key tool for the growth of any firm and accessing the right kind of financing according to the firm's need is of more vital importance. Access to finance for Small and medium enterprises (SMEs) is always a hot discussion for the researchers. Having known that SMEs are considered as backbone of economy and play key role for the growth and development of country, they face hindrances while accessing the finance from financial institutes. Keeping in view the importance of SMEs, this paper aims to widen the understanding on the available alternative financing options accessible to SMEs and entrepreneurs; this will enhance their knowledge about the full scope of financing instruments that they can access in various circumstances and by having healthy discussion among stakeholders about new methodologies and creative strategies for SME and business enterprise financing. Other than that, this paper also aims to highlights the SME demographics and role of government for supporting the SMEs.

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Keeping in view the importance of small and medium enterprises (SMEs) for the growth of a nation, we must also keep an eye on the challenges faced by those SMEs. There are various kinds of financing and supply chain options available for SMEs but they still face lot of hindrances. This paper would help us to understand why SMEs are important for the development of any country and how could we help the SMEs from facing challenges related to financing and supply chain. This study further highlights the key financing issues faced by SMEs and also focuses on major supply chain challenges confronted by the SMEs. This study put emphasis on the concept of supply chain finance (SCF) and that how SCF could help SMEs to overcome those challenges. In addition, this paper also points out the benefits and prospects of SMEs.  Even though the concept of SCF is still in developing phase but it has shown significant assistance to SMEs in order to grow further.

This article first expounds the concept of supply chain finance and its credit risk, describes the hierarchical structure of the Internet of Things and its key technologies, and combines the unique functions of the Internet of Things technology and the business process of the inventory pledge financing model to design the supply chain financial model based on the Internet of Things. Then it studies the credit risk assessment under the supply chain financial model based on the Internet of Things, and uses the support vector machine algorithm and Logistic regression method to establish a credit risk measurement model considering the subject rating and debt rating. Finally, an example analysis shows that the credit risk measurement model has a high accuracy rate for determining whether small and medium-sized enterprises in the supply chain financial model based on the Internet of Things are trustworthy. This will facilitate the revision and improvement of the existing credit evaluation system and improve the accuracy of measuring the current financial risk of supply chain. This research adopts the Internet of Things to measure financial credit risk in supply chain and provides a reference for the following researches.

This paper applies extreme value theory (EVT) to estimate the tails of return series of Chinese yuan (CNY) exchange rates. We find that the degree of fitting Pareto distribution to the data of the tail of return series is extremely high. The empirical results indicate that expected shortfall cannot improve the tail risk problem of value-at-risk (VaR). The evidence of back testing indicates that EVT-based VaR values underestimate the risks of exchange rates such as USD/CNY and HKD/CNY, which may be caused by the continuous appreciation of CNY against USD and HKD. However, compared with VaR values calculated by historical simulation and variance-covariance method, VaR values calculated by EVT can measure the risk more accurately for the exchange rates of JPY/CNY and EUR/CNY.expected shortfall, extreme value theory, historical simulation, value-at-risk, variance-covariance,