The Impact of Big Data Credit Technology

The Impact of Big Data Credit Technology: Comparison

Please note this is a comparison between Version 1 by Yuhuan Jin and Version 4 by Jessie Wu.

AsCredit rationing hindered the main force in boosting national economic development, micro and small enterprises (MSEs) play an irreplaceable role in stabilizingdevelopment 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 growth, narrowing the income gap, improving labor productivityman 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 promoting market competition. Compared with large and medium-sized enterprises,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 are numerous and widely distributed, which creates a broad job market for the labor force in both developing and developed countries. However,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 hindered the effective use of of MSEs, a simulation experiment is conducted to compare the differences in the credit rationing of MSEs with different credit resources and weakened MSEs’ incentive to engage inlevels 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 technological innovation and alleviate employment pressurey 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

1. Introduction

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 micro and small enterprises (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][4,5].

2. The Causes of the Credit Rationing of Micro and Small Enterprises

RegardingAs the cmauses of the credit rationing of MSEs, scholars generally conclude that specific characteristics of the credit demand side and credit supply side, as well as the economic policy regime, affect credit transaction costs and credit risks, making it more difficult for MSEs to obtain loans thanin 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. Demand-side factors mainly include entrepreneur characteristics, MSEs are numerous and widely distributed, which creates ^[6],a entebrprise size or ageoad job market ^[7][8], fownership type and legal form the labor ^[9], gefographic locatioce in ^[10],both industry affiliation ^[11],eveloping and assdevelopet structure ^[12]d countries. ThHowe literature shows that a deterioration in an enterprise’s own view of its ver, credit rationing hindered the effective use of credit history, economic outlook, and capital should reduce its access to financresources and weakened MSEs’ incentive to engage ^[13][14][15]. Iin addition, Beyhaghi et al. (2020) suggest that decreased profits increase the probability of an enterprise being rationedechnological innovation and alleviate employment pressure. ^[16]. There mechanism that leads tofore, alleviating the credit rationing of MSEs from the credit supply side lies in that, owing to information asymmetry, evaluating the credit risk of small enterprises is difficult for lend is an important issue that needs to be addressed urgently.

Thers ^[17].key Tto maximize profitability, lenders may apply stricter selection criteria and effectively alleviating the credit discrimination onrationing of MSEs ^[18]. Maisiak et al. (2019) and De Jonghe et al. (2020) revealed that, due to increased screening costs, smaller enterprises find it more difficult to access finance from banksto reduce the information asymmetry between MSEs and banks, ^[19][20].so The reasearch of Sun et al. (2013) affirmed the existence of “the discrimination of scale” in the process of SME financing, and showed that th to overcome the size disadvantage of MSEs, promote bank lending policies using fixed assets as collateral exacerbate the plight of small business financing, and discourage MSEs from defaulting; therefore, banks need ^[21]. Ectonomic policy regimes contribute to the credit rationingls to obtain the risk information of MSEs as evidenced by the fact that financial institutions may restrict credit or charge risk premiums for enterprises that operate opaquely in economies where legal regimes do not adequately protect property rights, institutions operate inefficiently, and the regulatory system is imperfect ^[22][23][24]. European ev. The emergence of big data brings an opportunity to address this issue and make enterprise size no longer a constraint restrictidence suggests that unique structural features combined with strict governance rules make MSEs leg the credit acquisition of MSEs [6]. Ussing attractive to external financiers, and, as a result, this results in difficulties in accessing credit for them ^[25]. Bbig-data-based credit technology, banks can efficiently analyze more thased on the African context, Simba et al. (2023) suggest that, due to vast institutional voids, unco-ordinated domestic policies and the widespread application of derivative accounting practices in financial markets, the availability of financial resources for small enterprises can be dangerously low ^[26]. Alrillions of bytes of relevant information, thus improving loan approval efficiency and reducing information asymmetry. Therefore, banks could implementhough scholars have explained the causes of the credit rationing of MSEs credit technology innovation based on different perspectives and contexts, however, most studies did not make a clear distinction between MSEbig data to predict risks and small and medium-sized enterprises (SMEs); the research on the credit rationing of MSEs is not systematic and in-depth. The concept of MSEs is derived from SMEs, and the explicit definition of MSEs is relevant to the understanding of the country’s economic structure and development, as well as to the allocation of resources and the identification of targets for government 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.

Exisupport. Thising research is different fromhas demonstrated the previous study. Focusing on the specificities of MSEs, researchers explain the persistence of credit rationing for MSEs under the traditional ospects for the widespread use of big data in the credit mode through an evolutionary game model. And researchers found that the credit strategies evolutionary trajectoryarket, but has not considered the impact of banks and MSEs under the traditional mode is extremely unstable and cannot reach equilibrium.

3. Countermeasures to Alleviate the Credit Rationing of Micro and Small Enterprises

In ’ application of big data credit technolordergy to alleviate the on MSEs’ credit rationing of MSEs, scholars have put forward numerous countermeasures mainly for banks, governments, and MSEs. Policy recommendations forin depth; this paper attempts to explore the general mechanism of banks mainly include innovatingapplying big data credit technologies and providing loans to MSEs by large banks. Ferri et al. (2019) showed that transactional lending technologies increased enterprises’ y in alleviating the credit rationing, whereas soft information mitigated asymmetric information problems and improved enterprises’ access to credit; when soft inform of MSEs through evolutionary game models and simulation was incorporated into transactional lending technologies, small enterprises’ credit rationing significantly reduced ^[27]. Verexperiments. It is expected that this resea and Onji (2010) argued that large banks ch can provide differentiated financial services and credit stheoretical support to MSEs at different stages of development, and they have informationfor banks in implementing big data credit technology advantages, network advantages, and the advantage of sharing information costs across time, which is more conducive to establishing long-term and stable co-operative relationships with MSEs and providing themto alleviate the credit rationing of MSEs and create new profit growth points. In wlith services ^[28]. Hght owever,f large banks may face Williamson-type organizational diseconomies. Governmentthe credit rationing intphervention can reduce the investigation cost of banks to MSEs, make banks’ deposit liquidity management more flexible, and improve the allocation efficiency ofnomenon of MSEs and the opportunities that big data credit resources ^[29]. Gtechnolovernment ginterventions for MSEs are categorized into indirect and direct interventions. Indirect interventions include taking measures to reduce transaction costs or increase the supply of funds, while direct interventions mainly include credit subsidies and loan guaranteeses provide to address this issue, based on the bounded rationality economic man ^[30]. Dai et al. (2020) shypowed that tax incentives from the government motivate enterprises to invest in short-term development opportunities with high returns rather than in long-term projects with high returns and high risks; tax incentives save capital expenditures for MSEs, and indirectly reduce the financing costs of MSEthesis, the evolutionary game model of banks and MSEs under the traditional mode and big data credit technology is ^[31]. Beck and Demirgü-Kunt (2010) point out that, as a form of risk sharing, government subsidies can help to increase the cash flow of MSEs and mitigate the negative impacts of co-ordination failures among guarantee agencies or the over-concentration of credit resources provided by collaboratingnstructed, respectively, in this paper. By establishing the replication dynamic equation of the payoff matrix of banks ^[32]. Arping et al. (2010) examined the functioning mechanism of government credit guarantees on enterprise financing, noting that government subsidies ford MSEs, the equilibrium points of the model under the two credit guarantemodes are more effective than other interventionssolved, and the Jacobian matrix ^[33]. Credits rationing has restriconstructed the development of MSEs, and MSEs need to enhance their capabilities and utilize the environment to create appropriate financing opportunities to solve the problem ^[34]. Po analyze the stability of each equilibrium point; the evoluticy recommendations for MSEs mainly include borrowing from small and medium-sized financial institutions, utilizing informal finance, and engaging in relationship lending. For example, Lehmann et al. (2003) point out that it is easy to form long-term relationships between small and medium-sized financial institutions and MSEs, which can help to reduce collateral requirements for MSEs and information asymmetry, thusonary 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, ^[35]. Isaksson’s (2002) study showed that, although the amount of each loan received by small enterprises from informal finance is small, they lend more often to informal finance and have a higher utilization ofsimulation experiment is conducted to compare the differences in the credit rationing of MSEs with different credit funds ^[36]. Cucculelli vet al. (2019) argue that, by establishing soft-information-based and durable lendingls under the traditional mode and big data credit technology. The resulationships with banks, the likelihood of small enterprises experiencing credit rationing is significantly reduced ^[37]. In addits show that the credit strategies evolutionary trajection, Olufunso and Francis (2011) advised the owners ofry of banks and MSEs to improve their management capacity by attending seminars and training programs to prepare them for access to finance ^[38]. Ogawa eunder the traditional mode is extremely unst al. (2013) showed that trade credit is an important source of finance for young and small enterprises that have difficulty in obtaining bank loans ^[39]. Overall, alble and cannot reach equilibrium; though scholaers have provided many policy recommendations based on different perspectivesefore, it is difficult to alleviate the credit rationing of MSEs, and these policy recommendations have also played a certain role, however, o. However, under big data credit technology, when the whole, the overall credit rationinglevel of MSEs has not been thoroughly reduced in many countries, especially in developing countries. Therefore, further research is needed on countermeasures against the credit rationing of MSEs. This research is different from existing research, since researchers believe thatis 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 is a kind of market mechanism defect. As a major player in the market, banks are the main external financing channels for MSEs; to alleviatewill be effectively alleviated. When the overall credit level of MSEs is too low, the credit rationing of MSEs, banks desperately need tools to obtain the risk information of MSEs, and big data credit technology provides an opportunity for achieving this.

4. The Role of Big Data in the Credit Market

Improvementsstrategies evolutionary trajectory of banks and MSEs is relatively unstable; accordingly, it is diffin socialization and the emergence of social networking platforms such as Facebook, Twitter, and Pinterest, as well as the tendency for data and programs to be accessed and stored over the Internet rather than on computer hard drives, have contributed to the era of big data ^[40]. “Big data” rcult to determine whether big data credit technology can alleviate the credit rationing of MSEs. Nevertheless, the comparison refers to massive data sets that are difficult to extract, store, search, share, analyze, and process with existing software tools, which require greater storage space and time, as well as sophisticated methods and technologies for managing and analyzing ults of the credit rationing of MSEs with different credit levels under the ^[41]. Vtwolume, Variety, and Velocity (“3V”) is a common framework for describing big data ^[42]. In addicredit modes show thation to the “3V” characteristics of big data, in recent years, Veracity, Variability, and Value have become new dimensions of he credit rationing of MSEs is always lower under big data characteristics, and are even more challenging ^[43]. Reredit technology than unduced storage costs and the widespread availability of cloud solutions from well-known providers such as Amazon, Google, and Microsoft have had a positive impact on the adoption of r the traditional mode. Consequently, we come to the conclusion that big data credit technologies and methodologiey has ^[44]. Cloud computing solutions can be used for big data management, and provide opportunities for enterprises, especially small enterprises, which are often constrained by a lack of financial and organizational resources ^[45]. Fsignificant effect on allevinancial sectors can highly benefit from big data. They can access massive amounts of transaction data which can be processed to gain competitive advantages over their peers, enhancing the customer banking experience, risk analysis and mitigation, and operation and optimization ^[46]. Thting the credit rationing of MSEs. This conclusion enriches the theoretical re use of big data technology can break through the traditional modarch on the role of banks in dealing with information asymmetry. When big dataig data credit technology is applied toand the credit business, there is no human or subjective judgment factor; instead, through the analysis of the historical data that actually occurred, it can improve the amount of information and accuracy of the credit of borrowers to a certain extent. Jin et al. (2022) pointed out that the application of rationing mechanism of MSEs, which provides a theoretical basis for banks to apply big data credit technology in credit evaluation facilitates the provision of unsecured credit based on industrial chain credit ^[47]to achieve a win-win situation for both parties. For tThe financing of MSEs, the value of big data lies in their ability to alleviate the information asymmetry between banks and enterprises, enabling banks to discover more high-quality MSEs with low risks instead of making credit decisions based on the qualitative characteristics of loan applicants, thereby expanding banks’is study still has the following limitations: This paper demonstrates the feasibility of big data credit allocation for the MSE group, effectivelytechnology in alleviating the problem of credit rationing for MSEs, and, at the same time, maximizing the banks’ own profits. The research of Kshetri (2016) shows that the main reason why low-income households or microenterprises in emerging economies lack access to financial services is not because they lack creditworthiness but merely because banks lack data, information, and capabilities to access the creditworthiness of and effectively provide financial services to this financially disadvantaged group ^[48]. Tencenof MSEs only by constructing evolutionary game models and implementing simulation experiments. However, due to data availability, we did not empirically test t’s Weizhong Bank has launched the “Microparticle Loan” product for its target customer groups, which is a microcredit product based on big data credit technology, and the speed of issuance of the product can be as fast as 45 s, and the slowest speed can be 90 s, which enables customers to enjoy a safer, faster, and more convenient service. In summe theoretical model and conclusions of this paper by collecting real-world data related to the situation of credit rary, the existing literature provides many useful insights into the role of big data technology in the credit market, and the existing literature demonstrates thationing for MSEs before and after the implementation of big data credit technology can reduce the information asymmeies by banks. Futury between borrowers and lenders, thereby lowering the transaction costs of banks, and making the size of the enterprise no longer a constraint on the access to credit for MSEs. However, existing studies did not intensively consider the mechanisms by which banks’ use e research will consider quantifying the impact of banks’ application of big data credit technology affectson the credit rationing of MSEs. By comparing the evolutionary trajectories of bank–enterprise credit strategies under big data credit technology and the tradition by collecting real-world data to validate the theoretical mode through an evolutionary game model, and comparing the extent to which big data credit technology alleviates the credit rationing of MSEs with different credit levels through simulation experiments, researchers demonstrate thatl 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 technology can effectively alleviate the credit rationing of MSEsies is also an important aspect for future research to consider.