Small and micro-sized enterprises struggle with difficult and expensive financing. The key challenge is the information asymmetry between small and micro-sized enterprises and financial institutions. In recent years, with the generation of various data such as e-commerce transactions, logistics data, online transactions, etc., there has been a breakthrough for small and micro-sized enterprises to obtain loans from banks. As fintech companies cooperate with banks on credit data, efficiency, and operating costs, it is driving the development of expanded financing options for SMEs.
Among the online data that has become available in recent years, taxation data is among the most high-quality data with strong financial relevance, lowest risk of fraud, and the broadest coverage among enterprises. The taxation data of a company can reflect its business scale and position in the industry. If the tax-related billing data of a company can be obtained, dynamic data such as change of operational scope, and the company’s upstream and downstream relationships, can be deduced.
This article by Pintec Academy is based on the study of taxation data scenarios and presents the business model, problems in practice and development directions of taxation loan participants.
1. Official exploration of taxation loans
In 2015, the State Administration of Taxation issued a notice on implementing a “Bank-Tax Interaction” strategy to support the development of SMEs, which aimed at making it easier for them to obtain unsecured loans through taxation data.
The policy was intended to solve the problem that SMEs could not obtain credit due to insufficient collateral, and to provide credit funding for SMEs with good records of operations and taxation. On the other hand, it was expected to optimize bank credit structures by increasing the proportion of credit for trustworthy small and micro-sized enterprises. Meanwhile, bank-tax interaction would combine tax-paying behavior with the development of corporate financing, making the tax-paying behavior a credit asset for small and micro-sized enterprises. This would help establish a trustworthy value orientation for the whole society, and help drive the construction of social credit system, leading to win-win for enterprises, financial institutions, and tax departments.
Banks responded to the call and launched a series of bank-tax loan products. However, many bank-tax loan products were difficult to implement and problems listed below have arisen during the actual operations.
- Difficulty gaining data authorization from the Administration of Taxation
- Different data systems among different tax bureaus
- Unstandardized data in different tax administrations
- Increasing costs for banks from strict risk management
Besides taxation data, many banks will also re-evaluate companies’ credit risk with offline methods, which greatly increases the cost.
Shanghai Bank-Tax Interactive Information Service Platform is the first bank-tax interaction platform established by the government. There were already 46 bank members on the day of its establishment on September 20, 2017. According to “2018 Annual Report of Building a Rule of Law Government Report” by Shanghai Taxation Bureau, bank-tax interaction platforms signed cooperation agreements with banking regulatory bureaus and over 60 banking institutions in Shanghai. The total credit amount reached RMB1.11 billion, 135 enterprises received credit, 3,563 companies inquired about the platform and the loan balance was RMB471 million. The overall credit scale is still very small.
The launch of a series of bank-tax loan products has not substantially increased the credit loans to small and micro-sized enterprises. According to the statistic with 73 banks involved by Bzzk-research of Xiamen International Bank in 2018, the proportion of credit loans was 28.85% for joint-stock banks, 27.47% for state-owned banks, 16.35% for urban commercial banks and 9.78% for rural commercial banks. The overall proportion of credit loans is still very low.
2. Three business models and case studies under taxation scenario There are three business models for loans under the taxation scenario.
2.1 Bank direct access to taxation platform
The first model is the cooperation between self-built platforms by tax bureaus and banks, providing credit loan services to local small and micro-sized enterprises. As mentioned above, the model of direct connection between bank and tax bureaus has high marginal cost and is often limited to certain banks in a certain region, resulting in limited scale and region coverage for credit loan services.
2.2 Third-party data channel built by fintech companies Some fintech enterprises have begun to serve as the bridge between banks and taxation departments. Fintech companies, such as Vzoom Credit, build bank-tax interaction platforms and are authorized by enterprises to provide professional services for banks. In recent years, to effectively support financing for small and micro-sized enterprises, fintech companies are seeking ways to solve the information asymmetry between banks and enterprises by harnessing data screening, data mining and big data analysis technology. Using tax data as the core and combining data from juridical and commercial administrations, fintech companies can evaluate enterprises’ operations thoroughly. The credit evaluation system based on “tax for credit” and “Internet+ big data” has gradually been developed to help banks make credit decisions and help small and micro-sized enterprises obtain credit loans.
Banks welcome fintech companies as partners naturally. Banks can save developing costs on connecting with tax departments and eliminate cumbersome work such as tax data mining and cleaning. On the one hand, joint-stock banks and state-owned banks can save labor cost and reduce their heavy workload. On the other hand, for small banks who could not conduct bank-tax loan business due to limited fintech technology, it is a good chance to develop new business. An industry insider told Pintec Academy that it is a good opportunity for urban and rural commercial banks to improve their business performance and expand to cross-region operations. Data from Vzoom Credit shows that Vzoom Credit has collaborated with over 30 state and city taxation administrations and more than 150 banks and financial institutions to provide bank-tax service for more than 4 million small and micro-sized enterprises with cumulative loans of more than RMB250 billion.
However, there are still limits to this model. First, as with the bank direct access model, fintech companies such as Vzoom Credit target banks as the main clients. Only well-performing small and micro-sized enterprises can be reached by banks, whereas most long tail small and micro-sized enterprises cannot be covered. Second, depending on fintech companies’ outlets, banks are limited by the scale of fintech companies. Third, it is not enough to rely solely on taxation credit to fundamentally solve the problem of financing difficulties for small and micro-sized enterprises. It is still necessary to continuously improve the credit system for small and micro-sized enterprises, not only using taxation data, but also integrating multi-dimensional data from commerce, telecommunication, e-commerce, logistics and third-party payment based on the characteristics of the enterprises. For Vzoom Credit and alike players, the capability of multi-dimensional big data risk management still needs to be improved.
In response to the above limitations, Zeng Yuan, Co-Founder and Chief Commercial Officer of Vzoom Credit said that Vzoom Credit has been transforming its big data risk management from expert-driven to a hybrid of data-driven and expert-driven to all data-driven. The breakthrough that the institutions like Vzoom Credit are making is to establish and improve the credit system for small and micro-sized enterprise through multi-dimensional big data risk management. Although it is difficult to fully cover the long-tail clients with bank taxation loans, banks should only be one application scenario for credit technology in the future. More financial institutions will extend their loan services through credit technology development.
2.3 Attempts at digital invoice platforms
The third participant: digital invoicing platforms that are most likely to serve long tail clients.
Invoice data is another way to demonstrate companies’ business performance and operations. The digital invoice platforms provide services for banks and other financial institutions through the analysis of enterprise billing data on their platforms, and become important participants in tax credit services, such as Nuonuo Finance of AISINO Corporation and invoice cloud provider Best Wonder. Second-tier participants, including printing paper manufacturer Donggang, and business finance SaaS service provider Yonyou and Kingdee, also joined the market in recent years.
AISINO Corporation (SHA: 600271) has the most electronic invoice application projects in pilot cities. It has deployed electronic invoicing systems in first tier cities such as Shanghai, Guangzhou and Chengdu. Best Wonder has developed the biggest invoice cloud ecosystem platform in China and has served more than 1,000 business groups, 300,000 small and medium companies and 100,000 small and micro-sized companies. It received RMB517 million of investment from Alibaba and Tencent on March 25, 2019. The electronic invoicing scenario is obviously considered as the most optimistic scenario by the market.
Invoice digitalization is the growing trend. Invoice information can be used to more accurately calculate the company’s fiscal and taxation operations. Here are several advantages of invoice information.
1) Invoice data contains more accurate information about a company’s up and down stream business. The tax data can only reflect the annual and monthly tax expenditure of a company. Invoices can reflect the buyer and seller’s information through the invoice title, which can determine the company’s stability in business transactions.
2) Invoice data can dynamically reflect changes in supply chain. Tax data is often static data for the past 12 to 24 months, while invoice data shows dynamic changes over time. For example, when a company has been steadily sending invoices to another company, if the invoicing is suddenly interrupted, the invoice data may reveal problems in its supply chain.
3) Invoice details can indicate risks. Invoice details can be used to derive fraud risks and operational risks such as any changes in business category of the company and whether the expenses are reasonable. Meanwhile, it can also be applied to credit cycle management for clients to monitor abnormal expenditure and alert clients in time.
Since invoice data uses self-owned standards of invoice cloud platforms, data from Best Wonder and AISINO no longer has interface problems and geographical restrictions, and the ways of connecting with financial institutions are more diversified and long-tailed.
Platforms can jointly develop products with banks. Shanghai Pudong Development Bank and AISINO Nuonuo Finance released their cooperation project----SPDB Nuonuo Bank-Tax Loan to serve small and micro-sized enterprises. They can also be data sources and assist in risk control for banks and more financial institutions. Best Wonder is cooperating with Pintec and other fintech solution providers to provide traffic and credit decisioning assistance for ICBC, Bank of Communication, and Fosun Financial.
However, the limitations of this model is also obvious. Given the fact that Best Wonder and AISINO platforms cannot respectively cover all small and micro-sized enterprises, there is still the possibility that AISINO and Best Wonder lack data for high-quality small and micro-sized enterprises, which makes them unable to obtain loans, or adequate loans.
Taxation data and invoice data are high-quality data sets with strong financial relevance, low risk of fraud, and broad coverage of small and micro-sized enterprises. With the digitalization of tax data in recent years, players such as banks, taxation authorities, fintech companies, and digital invoice cloud platforms have joined the market. Based on their own advantages, these players have been serving small and micro-sized enterprises in different dimensions.
Financing for small and micro-sized enterprises is an important policy and market issue. With fintech solutions, it is now much easier for traditional financial institutions to acquire customers, control risk with big data, and make smart approvals. Invoices and tax data are just one set of financial data in credit evaluation. More comprehensive and diversified data analysis is needed to provide credit loans for small and micro-sized enterprises. With more and more open access to official data sources, it is possible that more companies will receive relevant authorizations in the future, contributing to financial services for small and micro-sized enterprises. The problem that traditional financial institutions “do not dare to lend, cannot lend, or will not lend” to small and micro-sized enterprises will become a thing of the past.