The Impact of Big Data Credit Technology

The Impact of Big Data Credit Technology: Comparison

Please note this is a comparison between Version 2 by Jessie Wu and Version 1 by Yuhuan Jin.

CrAs thedit rationing hindered the main force in boosting national economic 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, MSEs play an irreplaceable role in stabilizing 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 papergrowth, narrowing the income gap, improving labor productivity, 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 thepromoting 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 of MSEs, a simulation experiment is conducted to compare the differences in the credit rationing of MSEs with different hindered the effective use of 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 creditresources and weakened MSEs’ incentive to engage in 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 partiesical innovation and alleviate employment pressure.

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 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].

2. The Causes of the Credit Rationing of MSEs

Regarding the mcain 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 withuses 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 than large and medium-sized enterprises, MSEs are numerous and widely distributed, which creates a. Demand-side factors mainly include entrepreneur characteristics [11], benterproad job marketise size or age [12,13], for the labor fwnership type and legal form [14], geogrce in boaphic location [15], industhry developingaffiliation [16], and dasseveloped countriest structure [17]. HowThever, credit literature shows that a deteriorationing hindered the effective use of in an enterprise’s own view of its credit resources and weakened MSEs’ incentive to engage history, economic outlook, and capital should reduce its access to finance [18,19,20]. In addition technological innovation and alleviate employment pressure, Beyhaghi et al. (2020) suggest that decreased profits increase the probability of an enterprise being rationed [21]. Ther mefore, alleviatingchanism that leads to the credit rationing of MSEs is an important issue that needs to be addressed urgentlyfrom the credit supply side lies in that, owing to information asymmetry, evaluating the credit risk of small enterprises is difficult for lenders [22].

Theo key to effectively alleviating the maximize profitability, lenders may apply stricter selection criteria and credit rationing ofdiscrimination on MSEs [23]. Masisak to reduce the information asymmetry between MSEs andet 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 banks [24,25]. The reso as to overcome the size disadvantage of MSEs, promotearch of Sun et al. (2013) affirmed the existence of “the discrimination of scale” in the process of SME financing, and showed that the bank lending, and discourage MSEs from defaulting; therefore, banks need t policies using fixed assets as collateral exacerbate the plight of small business financing [26]. Econols to obtain the risk informationmic policy regimes contribute to the credit rationing of MSEs. The emergence of big data brings an opportunity to address this issue and make enterprise size no longer a constraint restrict 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 [27,28,29]. European evideng the credit acquisition ofce suggests that unique structural features combined with strict governance rules make MSEs [6]. Ulessing big-data-based credit technology, banks can efficiently analyze more thattractive to external financiers, and, as a result, this results in difficulties in accessing credit for them [30]. Based on trillions of bytes of relevant information, thus improving loan approval efficiency and reducing information asymmetryhe 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 [31]. Although Tscherefore, banks could implementolars have explained the causes of the credit technology innovationrationing of MSEs 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.

Exisdifferent perspectives and contexts, however, most studies did not make a clear distinction between MSEs 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 tinarg research has demonstratedets for government support. This work is different from the prospects for the widespread use of big data in the evious study. Focusing on the specificities of MSEs, we explain the persistence of credit rationing for MSEs under the traditional credit market, but has not considered the impactode through an evolutionary game model. And we found that the credit strategies evolutionary trajectory of banks’ application of big data credit tech and MSEs under the traditional mode is extremely unstable and cannot reach equilibrium.

3. Countermeasures to Alleviate the Credit Rationing of MSEs

In ology on MSEs’rder to alleviate the credit rationing in depth; this paper attempts to explore the general mechanism ofof MSEs, scholars have put forward numerous countermeasures mainly for banks, governments, and MSEs. Policy recommendations for banks applying big datamainly include innovating credit technology in alleviating the crediies and providing loans to MSEs by large banks. Ferri et al. (2019) showed that transactional lending technologies increased enterprises’ credit rationing of MSEs through evolutionary game models and simul, whereas soft information mitigated asymmetric information problems and improved enterprises’ access to credit; when soft information experiments. It is expected that this reswas incorporated into transactional lending technologies, small enterprises’ credit rationing significantly reduced [32]. Verarch and Onji (2010) argued that large banks can provide theoretical sdifferentiated financial services and credit support for banks in implementing big data creditto MSEs at different stages of development, and they have information technology to alleviate the credit rationing of MSEs and create new profit growth pointsadvantages, 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 them with services [33]. I However, large banks light of the cmay face Williamson-type organizational diseconomies. Governmedit rationing phenomenon of MSEs and the opportunities that big data cnt intervention can reduce the investigation cost of banks to MSEs, make banks’ deposit liquidity management more flexible, and improve the allocation efficiency of credit resources [34]. Governmedit technologies provide to address this issue, based on the bounded rationality economnt interventions 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 guarantees [35]. Daic et man hypothesis, the evolutionary game model of banks and MSEs under the traditional mode and big data credit technology is al. (2020) showed 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 MSEs [36]. Beck and Demirgü-Kunt (2010) ponstructed, respectively, in this paper. By establishing the replicint 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 dynamic equfailures among guarantee agencies or the over-concentration of the payoff matrix of bankscredit resources provided by collaborating banks [37]. Arping et and MSEs, the equilibrium points of the model under the twol. (2010) examined the functioning mechanism of government credit guarantees on enterprise financing, noting that government subsidies for credit modguarantees are solved, and the Jacobian matrix more effective than other interventions [38]. Credist construrationing has restricted to analyze the sthe development of MSEs, and MSEs need to enhance their capability of each equilibrium point; the evies and utilize the environment to create appropriate financing opportunities to solve the problem [39]. Polutionary 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 incy 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, thus alleviating the credit rationing of MSEs, [40]. Isa simulation experiment is conducted to compare the differences ksson’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 of credit funds [41]. Cucculellin et al. (2019) argue the credit rationing of MSEat, by establishing soft-information-based and durable lending relationships with different banks, the likelihood of small enterprises experiencing credit leve rationing is significantly reduced [42]. In addition, Olufuns under the traditional mode and big data credit technoloo and Francis (2011) advised the owners of MSEs to improve their management capacity by attending seminars and training programs to prepare them for access to finance [43]. Ogy. Thawa et results showal. (2013) showed that thrade credit strategies evolutionary trajectory of banks and MSEs under the traditional mode is is an important source of finance for young and small enterprises that have difficulty in obtaining bank loans [44]. Ovextremely unstable and cannot reach equilibrium; therefore, it is difficultall, although scholars have provided many policy recommendations based on different perspectives to alleviate the credit rationing of MSEs. However, under big data credit technology, whe, and these policy recommendations have also played a certain role, however, on the overallwhole, the credit levelrationing of MSEs is high, the credit strategies’ evolutionary trajectory of banks and MSEs will eventually evolve into “lend” and “repay”, respectively; as a result,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 work is different from existing research, since we believe that the credit rationing of MSEs will be effectively alleviated. When the overall credit level of MSEs is too low,is a kind of market mechanism defect. As a major player in the market, banks are the main external financing channels for MSEs; to alleviate the credit strategies evolutionary trajectory of banks and MSEs is relatively unstable; accordingly, irationing 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

Improvements is difficult to determine whether big data credit technology can alleviate the credit rationing of MSEs. Nevertheless, the comparison rn 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 [45]. “Big data” refersults of the credit rationing of MSEs with different credit levels under the tw 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 the [46]. Volume, credit modes show thVariety, and Velocity (“3V”) is a common framework for describing big data [47]. In addit the credit rationing of MSEs is always lower underion to the “3V” characteristics of big data, in recent years, Veracity, Variability, and Value have become new dimensions of big data credit technharacteristics, and are even more challenging [48]. Reduced stoloragy than under the traditional mode. Consequently, we come to the conclusion that e 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 big data credit tetechnologies and methodologies [49]. Cloud chomputinology has a significant effect on allevg 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 [9]. Financial secting the credit rationing of MSEs. This conclusion enriches the tors 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 [50]. The use oretical research on the rolf big data technology can break through the traditional mode of big data creditanks in dealing with information asymmetry. When big data technology andis applied to the credit rationing mechanism of MSEs, which provides a theoretical basis for banks to applybusiness, 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 big data credit technology to achieve a win-win situin credit evaluation facilitates the provision of unsecured credit based on industrial chain credit [51]. For the finatncion for both parties. Tng of MSEs, the value of big data lies in theis study still has the following limitations: This paper demonstrates the feasibility of big datar 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’ credit technology inallocation for the MSE group, effectively alleviating the problem of credit rationing of MSEs only by constructing evolutionary game models and implementing simulation experiments. However, due to data availfor 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 capability, we did not empirically ties to access the creditworthiness of and effectively provide financial services to this financially disadvantaged group [7]. Tencent’st the theoretical model and conclusions of this paper by collec 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 summary, the exing real-worldsting literature provides many useful insights into the role of big data related to the situation oftechnology in the credit market, and the existing literature demonstrates that big data credit rationing for MSEs before and after the implementation of big data credit technologies by banks. Futechnology can reduce the information asymmetry 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 tuo cre research willdit for MSEs. However, existing studies did not intensively consider quantifying the impact of the mechanisms by which banks’ applicationuse of big data credit technology onaffects the credit rationing of MSEs by collecting real-world. By comparing the evolutionary trajectories of bank–enterprise credit strategies under big data to validatecredit technology and the theoreticraditional 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 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, we demonstrate that big data credit technologies is also an important aspect for future research to considery can effectively alleviate the credit rationing of MSEs.