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Credit rationing hindered the development of MSEs. Big data credit technology creates a great opportunity to address this issue. Then, how does big data credit technology affect and to what extent can it alleviate the credit rationing of MSEs? Based on the bounded rationality economic man hypothesis, the evolutionary game model of banks and MSEs under the traditional mode and big data credit technology are constructed, respectively, in this paper, and the evolutionary trajectory of bank-enterprise credit strategies under the two modes are comparatively analyzed. The results show that it is hard to alleviate the credit rationing of MSEs under the traditional mode. However, under big data credit technology, when the overall credit level of MSEs is high, the credit rationing of MSEs will be effectively alleviated. When the overall credit level of MSEs is too low, it is difficult to determine whether big data credit technology can alleviate the credit rationing of MSEs. In order to verify the feasibility of big data credit technology in alleviating the credit rationing of MSEs, a simulation experiment is conducted to compare the differences in the credit rationing of MSEs with different credit levels under the two credit modes. We found that the credit rationing of MSEs is always lower under big data credit technology than under the traditional mode. Therefore, big data credit technology can effectively alleviate the credit rationing of MSEs under any circumstances. The research provides theoretical support for banks to apply big data credit technology to achieve a win-win situation for both parties.
- micro and small enterprises
- credit rationing
- big data
- evolutionary game
According to the credit rationing theory developed by Stiglitz and Weiss (1981), due to adverse selection and moral hazard caused by information asymmetry in the credit market, there is no monotonic linear relationship between the expected return on bank loans and the interest rate. When borrowers’ demand for loans is greater than banks’ supply of loans, banks will implement restrictions on borrowers through non-price instruments rather than raising interest rates to clear the market; as a result, for undifferentiated borrowers, some can obtain loans while others cannot, and borrowers who could not obtain loans still have no access to loans even if they are willing to pay higher interest rates or provide more collaterals [1]. Enterprise size is considered to be one of the most important indicators in determining the financing barriers of borrowers. Macmillan (1931) [2] suggested that enterprise size affects the financing accessibility of enterprises: the smaller the enterprise, the higher the probability of suffering from credit rationing [3]. Even if MSEs have growth potential, it is difficult for them to obtain credit support. “Financing is difficult and expensive” has been a major problem faced by MSEs [4,5].
As the main force in boosting national economic development, MSEs play an irreplaceable role in stabilizing economic growth, narrowing the income gap, improving labor productivity, and promoting market competition. Compared with large and medium-sized enterprises, MSEs are numerous and widely distributed, which creates a broad job market for the labor force in both developing and developed countries. However, credit rationing hindered the effective use of credit resources and weakened MSEs’ incentive to engage in technological innovation and alleviate employment pressure. Therefore, alleviating the credit rationing of MSEs is an important issue that needs to be addressed urgently.
The key to effectively alleviating the credit rationing of MSEs is to reduce the information asymmetry between MSEs and banks, so as to overcome the size disadvantage of MSEs, promote bank lending, and discourage MSEs from defaulting; therefore, banks need tools to obtain the risk information of MSEs. The emergence of big data brings an opportunity to address this issue and make enterprise size no longer a constraint restricting the credit acquisition of MSEs [6]. Using big-data-based credit technology, banks can efficiently analyze more than trillions of bytes of relevant information, thus improving loan approval efficiency and reducing information asymmetry. Therefore, banks could implement credit technology innovation based on big data to predict risks and identify MSEs according to the quantitative information residing in their information management system, rather than making credit decisions based on the qualitative characteristics of MSEs.
Existing research has demonstrated the prospects for the widespread use of big data in the credit market, but has not considered the impact of banks’ application of big data credit technology on MSEs’ credit rationing in depth; this paper attempts to explore the general mechanism of banks applying big data credit technology in alleviating the credit rationing of MSEs through evolutionary game models and simulation experiments. It is expected that this research can provide theoretical support for banks in implementing big data credit technology to alleviate the credit rationing of MSEs and create new profit growth points.
In light of the credit rationing phenomenon of MSEs and the opportunities that big data credit technologies provide to address this issue, based on the bounded rationality economic man hypothesis, the evolutionary game model of banks and MSEs under the traditional mode and big data credit technology is constructed, respectively, in this paper. By establishing the replication dynamic equation of the payoff matrix of banks and MSEs, the equilibrium points of the model under the two credit modes are solved, and the Jacobian matrix is constructed to analyze the stability of each equilibrium point; the evolutionary trajectory of bank–enterprise credit strategies under the two credit modes is obtained. In order to further verify the feasibility of big data credit technology in alleviating the credit rationing of MSEs, a simulation experiment is conducted to compare the differences in the credit rationing of MSEs with different credit levels under the traditional mode and big data credit technology.
The results show that the credit strategies evolutionary trajectory of banks and MSEs under the traditional mode is extremely unstable and cannot reach equilibrium; therefore, it is difficult to alleviate the credit rationing of MSEs. However, under big data credit technology, when the overall credit level of MSEs is high, the credit strategies’ evolutionary trajectory of banks and MSEs will eventually evolve into “lend” and “repay”, respectively; as a result, the credit rationing of MSEs will be effectively alleviated. When the overall credit level of MSEs is too low, the credit strategies evolutionary trajectory of banks and MSEs is relatively unstable; accordingly, it is difficult to determine whether big data credit technology can alleviate the credit rationing of MSEs. Nevertheless, the comparison results of the credit rationing of MSEs with different credit levels under the two credit modes show that the credit rationing of MSEs is always lower under big data credit technology than under the traditional mode. Consequently, we come to the conclusion that big data credit technology has a significant effect on alleviating the credit rationing of MSEs. This conclusion enriches the theoretical research on the role of big data credit technology and the credit rationing mechanism of MSEs, which provides a theoretical basis for banks to apply big data credit technology to achieve a win-win situation for both parties.
This study still has the following limitations: This paper demonstrates the feasibility of big data credit technology in alleviating the credit rationing of MSEs only by constructing evolutionary game models and implementing simulation experiments. However, due to data availability, we did not empirically test the theoretical model and conclusions of this paper by collecting real-world data related to the situation of credit rationing for MSEs before and after the implementation of big data credit technologies by banks.
Future research will consider quantifying the impact of banks’ application of big data credit technology on the credit rationing of MSEs by collecting real-world data to validate the theoretical model and conclusions of this paper. Furthermore, how external factors such as government policies, financial market competition, and economic trends affect the effectiveness of the implementation of big data credit technologies is also an important aspect for future research to consider.
This entry is adapted from the peer-reviewed paper 10.3390/jtaer18040097
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