How Big Data Helps Small and Medium Enterprises Obtain Financing Through Supply Chain Finance

The Challenges Faced by Small and Medium Enterprises (SMEs) and Supply Chain Finance

SMEs often face funding shortages due to their weak position in the supply chain, a lack of sufficient collateral assets, and challenges in credit assessment. Supply chain finance offers an effective solution to address this issue. It refers to the process in which financial institutions provide loans and other financial services to a company and its upstream and downstream partners in the supply chain. Through supply chain finance, enterprises, channels, and even the entire supply chain in need of funding can obtain financing and other financial services based on comprehensive evaluations by financial institutions. This financing process typically relies on low-liquidity assets and future cash flows, such as accounts receivable.

With the increasing popularity of the Internet, the globalization of supply chains, and the rapid advancement of information technology, data has become an indispensable resource in the supply chain, playing a crucial role across various industries. The successful implementation of supply chain finance requires a deep understanding of key stakeholders in the supply chain, especially accurate assessments of the potential and risks of financing targets. This makes the application of big data crucial. With the rapid development of emerging technologies in the financial field, digital risk control is undergoing a revolution. Emerging information technologies such as the Internet of Things (IoT), machine learning, and blockchain have laid a solid technological foundation for the application of big data in supply chain finance, enabling efficient data acquisition, analysis, and sharing.

The Application of Big Data in Supply Chain Finance

1. Big Data and Risk Assessment of SMEs

Accurately assessing the credit risks of SMEs is a crucial aspect of supply chain finance. Traditional credit ratings primarily concentrate on large enterprises, leaving SMEs with inadequate credit risk assessments due to factors such as limited credit history records. However, the application of big data effectively mitigates the issue of information asymmetry between SMEs and financial institutions, thereby assisting high-quality SMEs in obtaining financing.

Vzoom Credit Technology is an innovative supply chain finance company supported by advanced big data analysis technology. It utilizes big data technology to assess the risks of SMEs and provides supply chain finance services to over 20 million SMEs in China. The company collaborates with institutions such as tax bureaus and banks to create an online automated approval platform for enterprise financing, offering convenient financing channels for SMEs. The process of risk assessment for SMEs, along with the provision of financing advice and suggested amounts, is depicted in Figure 1. Once registered on the platform, Vzoom Credit Technology obtains various data from SMEs, including credit, taxation, business registration, legal records, and blacklists. Vzoom Credit Technology employs big data models to analyze this essential data and provides financing advice and amount recommendations to financial institutions. At the data analysis level, Vzoom Credit Technology applies traditional statistical modeling and machine learning technologies, combining tax data and fintech to develop models. The selected SMEs have a bad debt rate that is one-tenth lower than the industry average. By leveraging big data and machine learning algorithms, Vzoom Credit Technology expanded its coverage, optimized business processes, reduced risk control costs, and effectively reducing the probability and losses of risk events, thereby providing strong support for financial risk control.

Figure 1. Supply Chain Finance Service Process Flowchart of Vzoom Credit Technology

2. Comprehensive Financial Services Based on Data Integration

With the development of the supply chain, supply chain finance centered around leading enterprises or specialized financial service platforms has emerged. The central enterprises or platforms not only possess data from individual enterprises but also have data on cooperation and transactions between enterprises. They can leverage data integration advantages to optimize financing processes and provide comprehensive financial services.

“S Company” (the real name of the company is omitted for confidentiality reasons), one of the main financial service providers and digital platform sponsors in China’s virtual mobile manufacturing industry, exemplifies the integration of big data on platforms to provide financial services to SMEs. Figure 2 illustrates the process and scope of the comprehensive financial services provided by S Company. Small and medium-sized suppliers establish projects and sign the contracts on S Company’s e-commerce where company and project information were submitted for loan applications. The platform conducts research on the enterprises, analyzing their historical data to determine the appropriate financing amount. In addition to providing financing services, S Company can also provide customized supporting services to enterprises by integrating and analyzing data from the supply chain. This includes aspects such as supplier management, inventory, production, logistics, customs clearance, and tax declarations, resulting in enhanced efficiency for both enterprises and the supply chain as a whole.

Figure 2. The Comprehensive Service Process Flowchart of S Company

Advantages of Big Data in Supply Chain Finance

1. Comprehensive Data Support

Traditional risk assessments for SMEs typically rely on a limited amount of financial data and credit reports. In contrast, big data allows for the collection and analysis of data from multiple perspectives, drawing from diverse sources such as internet service platforms, financial credit institutions, upstream and downstream companies, and third-party logistics providers. Notably, Internet service platforms serve as a major source of data, as they facilitate business operations and transactions within the supply chain. As a result, these platforms accumulate a wealth of data related to relevant enterprises. Financial credit institutions are required to conduct due diligence on target companies when providing financial services such as loans and financing. In this process, they gather credit data, including third-party guarantees, credit-to-deposit ratios, transaction history, social attributes, and founder backgrounds.

2. Advanced Big Data Analysis Technology

With the advancement of technologies like machine learning and AI, the analysis and modeling techniques for big data have rapidly evolved, enabling more accurate assessment of credit risks for SMEs. This advancement provides increased opportunities for financing to high-quality enterprises with lower credit risks. For instance, Vzoom Credit Technology has developed a credit risk scoring model specifically for small and micro-enterprises. This model effectively identifies variables with strong predictive capability, providing concise yet insightful assessments. Its practical effectiveness is evident in its assistance to tens of thousands of SMEs in obtaining financing. Another example is JD Finance’s “Skynet” system, which employs spark graph computing technology to analyze customer relationship networks and risk behavior. The advanced technology of the system enhances the accuracy of risk assessment, thereby facilitating improved financing opportunities for high-quality enterprises.

3. Efficient Data Integration and Collaboration

Internet service platforms, such as large e-commerce platforms, and leading companies in the supply chain, have access to data from all companies within the supply chain, including transactional and collaborative data. By leveraging the advantages of integrated big data, these platforms can offer enterprises comprehensive financial services and facilitate cooperation throughout the entire supply chain. This not only helps enterprises obtain financing and reduce costs but also enhances the overall efficiency of the supply chain. Moreover, the core platforms and enterprises can utilize internet data sharing technologies to enable supplier sharing, production capacity sharing, and logistics optimization. These practices further optimize the processes of supply chain finance.

Risks and Deficiencies

Big data has brought convenience to supply chain finance, but at the same time, it also presents certain risks and deficiencies, such as data security issues and data quality problems.

1. Data Security Issues

The provision of financing and other financial services to SMEs based on big data relies on a thorough understanding of their information. After collecting enterprise data, financial institutions need to organize, verify and analyze the data, which can easily lead to data leakages. Leaked data can be exploited for illegal activities such as identity theft and fraud, resulting in losses for the enterprise’s customers. If the core confidential information of an enterprise is leaked, it can have a negative impact on its competitiveness and damage the reputation of financial institutions, ultimately eroding customer trust.

2. Data Quality Problems

While utilizing big data, it is crucial to ensure the accuracy of the data. The effective utilization of big data relies on the correctness and accuracy of the data. However, the vast and complex amount of data on the internet includes a certain amount of false information. In practice, it is possible for SMEs to engage in fraudulent activities to obtain financing. Such fraudulent behavior may involve the fabrication of orders, the use of internet technology to forge data, or the involvement in false transactions on e-commerce platforms, like the well-known practice of “brushing” on Taobao. In some cases, only one party participates in the transaction, colluding with a third-party logistics company to fabricate transaction certificates and create false data. These false pieces of information can lead financial institutions to allocate funds to high-risk enterprises, depriving high-quality enterprises of financing opportunities and causing financial losses to financial institutions and even the entire supply chain.

Conclusion

Big data can be applied to assess the credit risks of SMEs and provide comprehensive financial services. By utilizing the extensive scope and comprehensive nature of big data, along with advanced data analysis techniques, it becomes possible to accurately identify high-quality SMEs and assist them in obtaining financing. Through the integration and analysis of extensive supply chain data, the success rate and efficiency of SME financing can be improved, promoting enterprise development and facilitating cooperation across the entire supply chain. However, it is crucial to safeguard data privacy, improve data quality, and mitigate risks to prevent losses to enterprises and the supply chain when utilizing big data.

Reference

[1] Song, H., Li, M. and Yu, K. (2021), “Big data analytics in digital platforms: how do financial service providers customise supply chain finance?”, International Journal of Operations & Production Management, Vol. 41 No. 4, pp. 410-435.

[2] Song, H. (2021), “Supply Chain Finance (Third edition)”, China Renmin University Press.

[3] Wu, Z., Li, Q., Li, J. and Zeng, Z. (2020), “The Development of Supply Chain Financial Services for the Real Economy in the New Era”, Southwestern University of Finance and Economics Press.

The work described in this article was supported by InnoHK initiative, The Government of the HKSAR, and Laboratory for AI-Powered Financial Technologies.
(AIFT strives but cannot guarantee the accuracy and reliability of the content, and will not be responsible for any loss or damage caused by any inaccuracy or omission.)

Share this content

Address

Units 1101-1102 & 1121-1123,
Building 19W Science Park West Avenue,
Hong Kong Science Park,
Shatin, Hong Kong

Products & Solutions

People

About Us

Address

Copyright © 2024 Laboratory for AI-Powered Financial Technologies Ltd. All Rights Reserved.