From a theoretical perspective, there are two root causes for the financing difficulties of small and micro enterprises: information asymmetry between the supply and demand of funds and incompatibility of incentives in risk management. However, in the era of big data, these two problems are emerging. Open Real Big Data to Solve the Financing Difficulties of Small and Micro Enterprises.

The Internet financial industry based on big data applications is relying on the channel advantages of the Internet open platform and the core competitiveness of data mining to try to break through the information and cost shackles that restrict the financing of small and micro enterprises, and to a certain extent reverse the asymmetry of credit resource allocation pattern.

The openness and sharing of data is a typical feature of the era of big data, and the United States is one step ahead in this regard.

Sometime in March 2009, the US federal government data open portal was launched to open all public data owned by the government to the public. As of December 2011, Data.Gov has released 3721 items of original data and 386429 items of geographic data.

Of potentially large data resources are very rich, from the department of tele-communications, finance, social security, real estate, health care, credit system, etc., to e- commerce platform, social networking sites, covering a wide range. Breaking the information asymmetry pattern between small and micro enterprises and financial institutions requires comprehensive social information to support.

However, the data disclosed in my country at this stage is only partial and fragmented, especially the social credit system, which is essential for evaluating the credit of small and micro enterprises, is still regionally fragmented and low in transparency.

Related Posts

As Internet financial institutions no longer rely on manpower, Internet financial institutions rely heavily on the acquisition of public information, so the high cost of acquiring information will undoubtedly stifle the development of small and micro finance.

Taking a typical P2P model as an example, with the help of latecomer advantages, the transaction process and mechanism of my country’s P2P lending platform are completely in line with the international advanced level. However, they often face the dilemma of insufficient public information in credit evaluation.

With a sound, complete and open credit investigation system and a mature credit rating market as a guarantee, the US P2P platform can completely outsource the credit evaluation module. Information ecology is a key constraint factor that determines how much P2P organizations can perform in the future.

The collection and networking of social information and the open sharing of social information must be led by government agencies due to the complexity of its overall planning. In the era of big data, public data should be opened to the public as a public resource. Only in this way can the potential value contained in the data be exploited and utilized to the maximum extent.

The availability of data is a prerequisite for the application of big data, and the authenticity of data is also of key significance.

Under the background of my country’s current tax system, small and micro enterprises deliberately misrepresent their operating and financial status in order to avoid taxes and fees or strive for preferential policies, so that the enterprises have no accounts to check.

The phenomenon of two sets of accounts for one company is quite common. The financial statements formed in this way cannot truly reflect the operating conditions of the enterprise and form a strong information asymmetry.

Not only that, speculative financial fraud has the effect of “bad money driving out good money” in the credit market. Counterfeit companies are often willing to bear high interest rates on loans to maximize speculative gains, which will gradually increase companies with good financial status and honest operations. Crowd out the market.

This mass production and disorderly flow of invalid data has seriously disrupted the normal order in the era of big data, and has also had a bad influence on data mining. Therefore, creating an ecological environment conducive to the production of real data by enterprises is undoubtedly of great value for improving the financing conditions of small and micro enterprises.

Continue Reading

From the government level, reducing the tax burden of small and micro enterprises is a top priority; from a market perspective, strengthening market supervision of small and micro enterprises and increasing the cost of falsifying data are also feasible directions for improvement.

In short, using the government to drive the market, optimize the ecological environment for small and micro enterprises to survive, and encourage the production of real data will be the only way to promote financing for small and micro enterprises in the era of big data.

The “big data + finance” model is essentially similar to the many existing ways to solve the financing problems of small and micro enterprises, all in order to create a low-cost market structure with completely symmetric information.

From a “pan-big data” perspective, whether it is the joint insurance, mutual insurance model, or the concept of social networks and business circles, these relational information that has been proven in the market to help improve the financing capabilities of small and micro enterprises can be used.

Incorporate into the dynamic warehouse of big data and extract the substantial content that is valuable for credit evaluation. The flat online world and the powerful ability of big data to control customers make credit resources no longer scarce.

However, we must clearly realize that until the social public data information is truly networked, open and shared, and systems and mechanisms that encourage real data production are truly established, the wide application of big data in the financial field can have a more suitable ecological environment. In order to truly enter the era of financial “popularization” and the era of big data.