Frontiers in Business, Economics and Management ISSN: 2766-824X | Vol. 20, No. 3, 2025 83 Research on the Path of Digital Empowerment in Credit Supervision Genli Shu * College of Economics and Management, Tianjin University of Technology and Education, Tianjin, 300222, China * Corresponding author: Genli Shu (Email: 2841899792@qq.com) Abstract: In recent years, with the in-depth advancement of the reform of "streamlining administration, delegating power, improving regulation, and optimizing services", new industries, business forms, and business models have achieved vigorous development. At the same time, the emergence of new technologies such as the Internet, big data, and blockchain has led to a disruptive transformation in credit supervision. By conducting a detailed analysis of the innovative practices of market supervision authorities in Zhejiang, Tianjin, Shandong, and other regions, this paper reveals that digital empowerment in cre dit supervision still faces many challenges in innovating supervision models, improving data governance, and enhancing supervision efficiency. Furthermore, it explores new paths for digital empowerment in credit supervision, promotes the sustainable development of digital credit supervision, and contributes to the realization of modernization in market supervision. Keywords: Digitalization; Credit Supervision; Data Governance; Supervision Innovation. 1. Introduction In the current digital era, credit is a fundamental element, and credit supervision is a major supervision method. Under the background of the new era, market supervision is different from the market management under the previous system. Only by continuously strengthening the extensive application of modern scientific and technological information technology in the field of market supervision can we adapt to the new changes and new normal of market order and achieve effective and efficient supervision. As a core component of the modern market supervision system, credit supervision's transformation and upgrading with the help of digital technology is an inevitable requirement to conform to the development of the times. Digital empowerment in credit supervision helps break the information barriers of traditional supervision models, improve the scientificity, accuracy, and effectiveness of supervision, and is of great significance for maintaining market order, preventing financial risks, and promoting the construction of a social credit system. However, the current digital credit supervision is faced with multiple challenges, such as the incomplete system of relevant laws and regulations, the increased difficulty in data governance, and the urgent need to establish a mechanism for cost - benefit optimization and effectiveness evaluation. In view of this, this study focuses on the challenges faced by digital credit supervision, deeply analyzes the existing problems and their root causes in current practices, and then puts forward targeted promotion paths to further improve the theoretical system of digital credit supervision and lay a solid theoretical foundation and practical support for building a high - level socialist economic system in the new era. 2. Research on Credit Supervision and Digital Credit Supervision In recent years, the domestic academic community has conducted in - depth research in the field of credit supervision in China, and has achieved remarkable results in terms of the basic concepts, core contents of credit supervision, and the social and economic significance implied in the implementation of credit supervision. Regarding the basic concept of credit supervision, it originated in the field of financial credit investigation, and then rapidly expanded in the scope of market supervision, finally forming a new type of market supervision paradigm based on credit and suitable for multiple fields [1]. The academic community has a broad and narrow definition of the connotation of credit supervision: from the narrow perspective, it focuses on the whole - process handling of market entities' credit information by supervision authorities, that is, from information collection and evaluation to classified supervision and implementation of rewards and punishments, so as to achieve the supervision goals [2]; from the broad perspective, credit supervision takes behavioral facts and legal norms as the basis for governance, relies on credit record data to build the foundation of governance, and takes the credit behaviors of natural persons and legal persons that meet the statutory conditions as the target of governance [3, 4]. Its core essence is reflected in the three - party collaboration among the government, society, and market entities, and runs through the three - dimensional supervision context of pre - event prevention, in - event control, and post - event disposal [5]. Undoubtedly, credit supervision is not only a key part of building a modern economic system, but also an inherent requirement for promoting the modernization of the national governance system and governance capacity [6]. In terms of the practical effectiveness of credit supervision, relevant functional departments can carry out accurate targeted supervision based on the credit portraits of market entities, reduce the excessive intervention of the government in the micro - operation of enterprises, thereby activating the internal driving force of the market [7], and laying the foundation for creating a high - quality business environment and building a unified national market [8]. Other scholars have pointed out that credit supervision also plays an important catalytic role in the improvement of the social credit system and the optimization of social governance [9]. In the context of the rapid development of digitalization, although the research on digital credit supervision is still in its infancy, some foresighted scholars have begun to explore 84 the realization paths of credit digital transformation and data - empowered supervision. For example, they try to embed credit digital construction into the entire chain of public services, integrate it deeply into the process of digital transformation of social governance, and build a credit supervision process that conforms to the underlying logic of government digital transformation [10]; promote the in - depth integration of cutting - edge information technology and supervision business, and open up an innovative path of "intelligence + credit" supervision for coordinated development [11, 12]. The digital transformation of credit supervision is expected to create a new supervision pattern where a capable government and an effective market complement each other, and help form a fair supervision ecosystem [13, 14]. Based on the above research, the current academic research on credit supervision has clarified the concept, expansion context, and core contents, and also pointed out its key value in the economic and social governance aspects. However, in the cutting - edge field of digital empowerment in credit supervision, the research is still weak. At present, only a few scholars have touched on topics such as credit digitalization and data - empowered supervision, and the exploration is not in - depth, and the results are difficult to form a system, which cannot meet the urgent needs of the country to improve digital governance capacity and strengthen the effectiveness of credit supervision. Therefore, starting from the actual needs of digital credit supervision, this paper will clarify the practical challenges of digital empowerment in credit supervision, explore a comprehensive and multi - level new path of digital empowerment in credit supervision from theory to practice, and lay a good foundation for improving the modern supervision system. 3. Practical Cases of Digital Empowerment in Credit Supervision In the current digital era, market supervision authorities across the country are actively exploring the path of digital transformation, improving the efficiency of market supervision, and accelerating the process of modernization of market supervision capacity. In this process, the market supervision authorities in Wencheng (Wenzhou, Zhejiang), Tianjin, Dezhou (Shandong) and other places have stood out and formed a series of representative practical cases of digital empowerment in credit supervision. 3.1. Wencheng (Wenzhou, Zhejiang): Integrating Digital Credit and Innovating the "Unperceived Supervision" and Accurate Service Mechanism The Market Supervision Bureau of Wencheng County has vigorously promoted the construction of the digital credit system for enterprise entities, focused on creating a new "digital + credit" unperceived supervision model, and built a digital supervision pattern of "interconnection, co - construction and sharing". Firstly, integrate resources to support digital supervision. Relying on the "Zhejiang Enterprise Credit Online" supervision platform, the enterprise credit information supervision and early warning system, etc., it breaks the "island" situation of fragmented, decentralized and regionalized enterprise information, forms a panoramic and multi - dimensional portrait of enterprises, thereby realizing the scientific allocation of supervision and law enforcement resources and providing strong support for accurate supervision. Secondly, optimize the government - bank - enterprise connection to realize "loan granting based on credit". Wencheng County has actively optimized the three - party connection model among the government, banks and enterprises, collected digital resources and calculated credit scores with the help of the "Xiaoweitong" platform, accurately screened market entities with good credit, and pushed them to the banking system, which has significantly reduced the resistance in the intermediate links of credit. Thirdly, establish a joint service mechanism for credit repair. Wencheng County has established a joint service mechanism for credit repair and specially set up a green channel for credit repair. In the process of case investigation and handling, it actively promotes the "three documents delivered simultaneously" policy, that is, when delivering the administrative penalty decision to the administrative counterpart, it simultaneously delivers the credit repair notice for administrative penalty information and the administrative compliance proposal, which has greatly stimulated the willingness of untrustworthy entities to be honest and trustworthy. 3.2. Tianjin: Integration and Innovation of "Internet + Supervision" and Credit Supervision With its forward - looking "Three Ones" strategy, Tianjin has in - depth promoted the practical exploration of "Internet + Supervision" and made every effort to build a new intelligent supervision system with "digital + credit" as the core. Firstly, strengthen the top - level design of the digital government. Promote the "three - level guarantee, four - level construction, and multi - level improvement" network information work system, establish the Tianjin Municipal Big Data Management Center, accelerate the construction of the digital government, build a network information and big data work pattern, and ensure the effective integration and efficient use of data resources. Secondly, break data barriers. Relying on the national integrated online supervision platform, it connects the national "Internet + Supervision" platform with Tianjin's "Internet + Supervision" platform, realizes the coordination between the central and local governments, breaks the horizontal and vertical data barriers, provides Tianjin with rich data resources, and lays a foundation for exploring the new "digital + credit" coordinated supervision model. Thirdly, standardized management of data. By establishing a city - wide standardized list of "multi - list integration" corresponding items, it connects systems such as "Credit China (Tianjin)", "Internet + Supervision", and "Law Enforcement Supervision Platform". Based on the completed "laws and regulations database", it has formed more than 17,000 "multi - list integration" corresponding items, which not only reduces the burden on the grass - roots level, but also comprehensively improves the standardization level of supervision and provides a standardized basis for accurate supervision. 3.3. Dezhou (Shandong): Innovative Practice of Credit Risk Classification Supervision for Individual Industrial and Commercial Households The Market Supervision Bureau of Dezhou City has actively explored to strengthen the "digital + credit" 85 empowerment, promoted the innovative practice of credit risk classification supervision for individual industrial and commercial households, and provided strong guarantee for the healthy development of individual industrial and commercial households. Firstly, build a credit risk classification platform. Establish a credit risk classification platform for individual industrial and commercial households, and build a general classification index system covering 5 aspects: registration and licensing information, operation and change information, punishment and negative list information, complaint and report information, and evaluation and commendation record information. Secondly, strengthen dynamic management. On the basis of the initial classification, implement the whole - process dynamic management of promotion and demotion, deduct credit scores for different untrustworthy behaviors, and dynamically adjust the credit classification of individual industrial and commercial households every quarter, so as to promote individual industrial and commercial households to enhance their awareness of law - abiding operation and honest operation, and further implement the main responsibility. Thirdly, promote the application of risk classification results. Actively promote the transformation from data analysis to data application, and widely apply the risk classification results to government supervision and services for benefiting the people and enterprises. In addition to the above - mentioned regions, there are many other valuable practical cases of digital empowerment in credit supervision across the country. For example, the Market Supervision Bureau of Nanjing City (Jiangsu Province) has realized the centralized management and sharing of market supervision data by building a "smart market supervision" platform. The platform has functions such as risk early warning and intelligent analysis, which provides strong support for market supervision work. The Market Supervision Bureau of Shenzhen City (Guangdong Province) uses big data analysis technology to conduct credit scoring and classification management of market entities, and implements differentiated supervision measures, which further improves the pertinence and effectiveness of supervision. To sum up, the practices in these regions fully show that digital empowerment in credit supervision has become an important means to promote the modernization of market supervision, and provides strong support for improving the efficiency of market supervision and optimizing the market environment. 4. Challenges Faced by the Development of Credit Supervision in the Digital Era The implementation of digital credit supervision has undoubtedly brought revolutionary changes to the field of market supervision, and its effects have gradually emerged in many places. However, the development of any new thing is accompanied by challenges. While achieving some results, digital credit supervision also faces many challenges that need to be solved urgently. 4.1. Legal and Model Shortcomings of Digital Credit Supervision 4.1.1. Incomplete Laws and Regulations Related to Digital Credit Supervision At present, digital transaction models emerge in an endless stream, constantly reshaping the business ecology, but the corresponding credit supervision laws and regulations are seriously lagging behind. On the one hand, there are many institutional gaps in the current legal system. Faced with credit problems derived from emerging digital business forms, it often falls into a dilemma of having no rules to follow. For example, there are no clear laws and regulations to rely on for credit rating disputes in some new online consumer credit scenarios. On the other hand, the effectiveness level of existing credit laws and regulations is generally low, mostly in the form of departmental rules and local laws and regulations, lacking the strong support of high - level national laws. This makes their ability to restrict supervision seriously insufficient. In the face of cross - regional and large - scale digital credit violations, the punishment is weak and it is difficult to form an effective deterrent. 4.1.2. Disconnection in the Connection between the Old and New Models of Digital Credit Supervision With the rapid application of digital technology in the field of credit supervision, it inevitably leads to the collision and running - in between the old and new supervision models, resulting in the problems of connection disconnection and supervision blind spots. On the one hand, the efficiency and convenience brought by new technologies are very attractive. Functions such as instant processing of massive data and real - time push of risk early warnings make some supervision departments unconsciously devote more energy to technical means, thus ignoring the importance of traditional supervision methods, leading to the weakening of on - site visits and communication supervision. On the other hand, the supervision work focuses too much on technical solutions, so the subjective initiative of people and the irreplaceability of on - site supervision are increasingly marginalized. Although technology can capture data abnormalities, it is difficult to reveal the deep - seated problems behind complex business transactions. 4.2. Difficulties in Data Governance of Digital Credit Supervision 4.2.1. Difficulty in Ensuring Data Quality and Security In the digital credit supervision system, the uneven quality of data is the primary problem. On the one hand, data comes from a wide range of sources, including government departments, financial institutions, e - commerce platforms, enterprises themselves and other multi - subject entities. Due to the fact that different entities collect information for different business purposes and data collection standards, the data formats and calibers are inconsistent, which makes the integrated data prone to deviations when used for credit evaluation. On the other hand, data update is not timely. The business status of market entities changes rapidly, but the update frequency of some data sources is low. For example, the information on the change of business scope of enterprises in some local industrial and commercial registration systems may be delayed for several months due to the cumbersome manual review process. If credit supervision decisions are made based on old information during this period, it is very likely to mislead the supervision direction, ignore potential risks or wrongly punish compliant enterprises. 4.2.2. Coordination Dilemma Credit supervision in the digital economy is still in its infancy. Some regions and departments carry out information work within their own regions (or units), which will 86 inevitably lead to data islands and data silos. Firstly, the data standards of various departments are inconsistent, and the differences in data definitions and statistical calibers make it difficult to match and integrate data when sharing. Secondly, the division of data sharing rights is vague. Considering data security and departmental interests, some departments are cautious about data sharing, and are not clear about which data can be made public to the outside world and how to define the sharing level, leading to blocked data circulation. Finally, the game of departmental interests is obvious. Some departments that hold key data regard data as a "resource chip", worrying that they will lose their data advantages and management voice after sharing, and are unwilling to take the initiative to share. For example, between financial supervision departments and local government departments, there are often interest disputes in the sharing of financial institution risk data, making it difficult to collect the comprehensive data required for digital credit supervision and affecting the play of supervision efficiency. 4.3. Bottlenecks in Performance Improvement of Digital Credit Supervision 4.3.1. Difficulty in Cost Optimization In the process of construction and operation of the digital credit supervision system, cost control constitutes a primary and continuous challenge. In the early stage of the construction of the digital credit supervision system, the primary task is to build a provincial - level credit data center. However, the construction cost of hardware infrastructure such as data centers, servers, and network equipment are particularly significant. In order to meet the needs of data storage and processing, it is also necessary to be equipped with massive storage equipment and high - performance computing clusters. At the same time, there is also a huge investment in software development, involving many links such as credit scoring models, risk early warning systems, and data management platforms. In order to adapt to the dynamic changes of the digital economy, the software also needs continuous iteration and upgrading, and the investment will continue to increase. In the operation and maintenance stage, with the exponential growth of data volume, the costs of data storage, backup, and transmission continue to rise. Regular data cleaning and verification to ensure data quality require a lot of human and computing resources. At the same time, the daily inspection, fault repair, and security protection of the system require the support of a professional operation and maintenance team, and the investment in human and material resources is continuous. 4.3.2. Difficulty in Effectiveness Evaluation The effect of digital credit supervision is affected by the interweaving of multiple complex factors, making it difficult to accurately quantify and evaluate. Firstly, the improvement of the credit status of market entities is a long - term and gradual process, which is affected by many external factors such as economic cycle fluctuations, industry competition, and enterprise strategic transformation. It is difficult to separate the specific effect of the single factor of digital supervision on credit improvement. Secondly, digital supervision exerts its strength through the coordination of multiple links, such as data monitoring, risk early warning, and joint punishment, which complement each other. It is difficult to accurately distinguish the independent contribution of each link to the final supervision results, which increases the difficulty of quantitative evaluation. Finally, some evaluation indicators focus on the quantification of the supervision process, but ignore the measurement of the supervision result orientation. For example, key indicators such as the suppression rate of untrustworthy behaviors and the improvement of the sense of gain of market entities in abiding by trust are missing or have low weights, which cannot accurately present the substantial effect of supervision on optimizing the market order. 5. Path Selection for Digital Empowerment in Credit Supervision 5.1. Improve the Legal System and Optimize the Supervision Model 5.1.1. Improve Relevant Laws and Regulations In order to meet the needs of digital credit supervision, we should accelerate the improvement of the legal system for digital credit supervision. Firstly, China should step up the formulation of the "Digital Economy Credit Supervision Law" to clarify the legal responsibilities of digital platforms in the whole life cycle of data, that is, in the links of data collection, transmission, storage, and use. It stipulates that platforms should report data management situations to supervision departments regularly, so that supervision agencies can accurately control the flow of data and realize the whole - process data traceability. Secondly, comprehensively revise the existing laws and regulations. In the field of credit investigation, technical specifications for ensuring the immutability of credit investigation data by using blockchain technology should be supplemented in a timely manner; in the scenario of big data analysis, the specific requirements for data desensitization and encryption should be clarified, so that laws and regulations can keep pace with the development of cutting - edge technologies. Finally, refine supporting implementation rules. For the data collection link, accurately target the characteristics of different industries and diverse application scenarios, clearly define the scope of collectible data, and explicitly prohibit the excessive collection of irrelevant information such as users' social data. 5.1.2. Promote the Integration and Innovation of Models The integration and innovation of digital credit supervision and traditional supervision models has become an inevitable way to optimize supervision efficiency, and it is urgent to promote it in an all - round and systematic way. Firstly, through regular organization of cross - departmental training and seminar salons, promote in - depth communication between traditional supervision personnel and digital technology teams, learn from each other's advantages, break the thinking barriers, and integrate the traditional on - site inspection experience with the digital dynamic monitoring thinking. Secondly, build a physical platform for the integration of digital credit supervision and traditional credit supervision, smooth the technical connection, integrate the existing scattered systems, and build an integrated "digital credit supervision comprehensive platform". Finally, cultivate compound talents to inject impetus into model integration. Colleges and vocational schools should set up the major of "Digital Economy Credit Supervision", offer courses such as legal practice of data compliance, big data analysis technology, and digital economy industry analysis, and cultivate professional talents who understand both legal rules and digital technology; at the same time, in - service training should be transformed into a compound type, and organize 87 supervision personnel to participate in the "digital supervision practical training camp". Through simulated case operation and on - site project training, the integrated business ability of the supervision team is improved to ensure the smooth operation of the new model. 5.2. Strengthen Data Governance and Collaborative Sharing 5.2.1. Improve Data Quality and Security Assurance Digital empowerment in credit supervision requires a large amount of supervision data resources as support. Firstly, it is necessary to do a good job in the collection and integration of data resources, so as to lay a solid foundation for the promotion of digital supervision. With the goal of "collecting all that should be collected", sort out and optimize the data resource catalog, clarify the responsibilities and obligations of each data source, ensure the reliability of data sources and unified formats, and accelerate the construction of data warehouses. Secondly, strengthen the operation of data quality. The data in grass - roots data supervision is complex and there are quality problems. Thirdly, to optimize data governance, we should improve the dynamic management mechanism of data quality, align with the data standards of the General Administration, and pay attention to the pre - verification of data quality. At the same time, build a standardized governance system such as bypass monitoring, problem distribution, and source repair, and form a closed - loop repair model across levels, departments, and fields. Finally, encourage scientific research institutions and enterprises to increase investment in privacy protection technology fields such as data encryption, desensitization, and anonymization, and develop new technologies that meet the needs of digital credit supervision. 5.2.2. Break Data Coordination Barriers The collaborative governance of data is directly related to the supervision efficiency and accuracy of digital credit supervision, and needs to be implemented from multiple dimensions. Firstly, unify data standards. Led by the national level, joint industry associations, leading enterprises, and technical experts to jointly formulate a unified data standard system. All departments and regions should transform and upgrade their own information systems in accordance with this guidance to ensure data interconnection and eliminate the barrier of "data dialect", so as to provide a standardized data foundation for digital credit supervision. Secondly, build a national integrated digital credit supervision data sharing platform, and extensively mobilize all levels of departments and business lines of the market supervision system from the central to the local to deeply integrate into it. Break the traditional working model of mutual independence and isolation between lines, enhance the service concept and systematic concept, and create a new operation mechanism of information exchange, knowledge sharing, and collaborative supervision. Finally, strengthen the construction of cross - regional cooperation agreements and coordination mechanisms to fill the data gap between administrative regions. Governments in various regions should uphold the concept of open cooperation, sign cross - regional credit data sharing cooperation agreements, and clarify key contents such as the scope of data sharing, interaction methods, update frequency, and security assurance measures. 5.3. Break Through Performance Bottlenecks and Improve Supervision Efficiency 5.3.1. Optimize Cost Management In the field of digital credit supervision, scientific budget planning is the primary link to control costs. Firstly, integrate existing resources. Break the barriers of information systems within departments, integrate the credit data collection, storage, and analysis systems scattered in various departments and business lines, avoid repeated construction, and realize one - time data collection and multi - party sharing. Secondly, explore a diversified investment mechanism. The government should play a guiding role, set up a special fund for digital credit supervision, provide subsidy support for key technology research and development and infrastructure construction projects, encourage the participation of social capital, and pilot the public - private partnership model in some regions, where the government and enterprises jointly invest in the construction of credit big data projects to ease financial pressure and stimulate market vitality. Finally, adopt a hybrid supervision model that combines manual supervision and automated technology. First, use the intelligent system to conduct a preliminary analysis of the credit data, assist in determining the entities with high - risk or abnormal behaviors, and then the manual team with professional knowledge and experience will conduct screening and judgment. This can significantly reduce the supervision cost, correct the accuracy of the system, and improve the processing efficiency. 5.3.2. Improve the Effectiveness Evaluation System In order to effectively improve the effectiveness evaluation system of digital credit supervision, a series of strategies need to be adopted to ensure its scientificity, comprehensiveness, and practicality. Firstly, comprehensively use diversified evaluation methods. In addition to traditional statistical analysis, make full use of big data analysis to tap the value of massive supervision data, and gain insight into the dynamic changes of supervision through technologies such as data correlation and trend prediction. Secondly, use technologies such as artificial intelligence and neural networks to model, continuously optimize the enterprise credit risk classification model, and realize automatic empowerment and intelligent classification. 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