Analysis of Beijing Tianjin Hebei regional credit system from the perspective of big data credit reporting

Liu, Chengyong; Chiou, Ling‐Jan; Li, Cheng-chung; Ye, Xiu-Wen

doi:10.1016/j.jvcir.2019.01.018

Cited by 12 publications

(9 citation statements)

References 22 publications

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“…Some scholars point out that peer-to-peer (P2P) lending in China improves the accessibility and availability of credit (Mi & Zhu, 2017;Ren et al, 2018;Zhang et al, 2017), while others find that digital finance provides an alternative funding mode for small-and medium-sized enterprises (SMEs) and households in China (Wang et al, 2018(Wang et al, , 2022 and in Indonesia (Rosavina et al, 2019). Still others point out that digital finance increases the efficiency of the financial industry by promoting a combination of Internet technology and financial business (Berger & Gleisner, 2009;Kshetri, 2016) and by continuous innovation with big data technology (Liu et al, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Cross‐regional comparative study on digital finance and finance efficiency in China: The eastern and non‐eastern areas

et al. 2022

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Digital finance is expected to improve the financial development of economic backward regions through its inclusive nature. Therefore, this paper selects the financial data of 30 provinces in China from 2011 to 2017 to evaluate regional finance disparities with the inclusion of digital finance, using the meta‐frontier slack‐based measure dynamic DEA model. The results are as follows. (1) The overall financial efficiency, input efficiency, output efficiency, and TGR in the eastern region are better than those in the non‐eastern region. (2) With the inclusion of digital finance, the finance efficiency gaps in the eastern and non‐eastern regions have expanded.

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Section: Literature Reviewmentioning

confidence: 99%

Cross‐regional comparative study on digital finance and finance efficiency in China: The eastern and non‐eastern areas

et al. 2022

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“…For this reason, the process of analyzing information to determine borrower eligibility should involve artificial intelligence. The platform can use big data to analyze the borrower's social media and obtain creditworthiness information from social media and e-commerce usage history data ( Liu et al., 2019a ). This happens because the P2P lending industry cannot access the credit score of prospective borrowers via official channels such as banks ( Ma et al., 2018 ).…”

Section: The Relationship Between the Findings And P2p Lending Practicesmentioning

confidence: 99%

Detection of fintech P2P lending issues in Indonesia

Suryono

Budi

Purwandari

2021

Heliyon

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Financial technology (fintech) is a growing industry in Indonesia, supported by advances in the technological infrastructure. At the end of 2019, the Financial Services Authority (OJK), the financial authority in Indonesia, recorded 164 registered and licensed fintech (P2P lending) companies. However, since early 2018, the Investment Alert Task Force (SWI) and the Ministry of Communication and Information Technology have blocked 1,350 illegal fintech platforms. Illegal fintech lending practices have mechanisms beyond the responsibility and authority of the OJK, including the risk of collection and distribution of personal data. The essence of this study is to discuss the landscape of fintech P2P lending in Indonesia from Indonesian Online News data, explore cases of fintech p2p lending in Indonesia, and understand the rules and policies. Qualitative research with a case study approach and Focus Group Discussion techniques were used to obtain data from 4 stakeholders in the Fintech P2P Lending Industry in Indonesia. VOS Viewer software is used to build keywords from Indonesian Online News collections, NVIVO 12 qualitative software is used to assist data analysis. The research found the keyword clusters most frequently discussed in the Indonesian Online News collection and five case themes such as public awareness about P2P lending (user understanding), data leakage, and restriction of data access, including personal data protection, personal data fraud, illegal fintech lending, and Product marketing ethics.

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“…The big data financial architecture is shown in Figure 1. The amount of data, the processing speed, and the richness of data types are the main characteristics of the large amount of data generated by the network, including not only structured data such as digital data but also unstructured data [15,16]. The arrival of big data has also increased the information resources of financial accounting and management [17].…”

Section: Enterprise Financial Asset Risk Measurementmentioning

confidence: 99%

Enterprise Financial Asset Risk Measurement Based on Embedded Microprocessor Security Analysis

Wang

2022

Wireless Communications and Mobile Computing

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As the volatility of financial markets continues to increase, the risk structure involved in financial derivatives has become more and more complex. The harmful effects of financial risks can put the company in trouble. Only by understanding the correlation between financial assets can we more accurately measure the risk of a company’s financial assets. This article is aimed at studying the company’s financial asset risk measurement method based on integrated microprocessor security analysis, so that enterprises can effectively manage the risk of financial assets. This paper randomly selects 10 variables related to demographic characteristics, customer needs, and consumption characteristics of 850 customers from the historical customer data of a bank that has been abandoned for customer market segmentation research. Based on the theoretical basis of customer value evaluation, the factor analysis method refers to a multivariate statistical analysis method that starts from the internal dependence relationship of the research index correlation matrix and reduces some variables with overlapping information and intricate relationships into a few unrelated comprehensive factors. The basic idea is to group variables according to the size of the correlation, so that the correlation between the variables in the same group is higher, but the variables of different groups are not correlated or correlated, and each group of variables represents a basic structure—the common factor. This paper uses the factor analysis model in statistical principles to analyze the main factors affecting the pricing of deposit products, and we can obtain three quantitative common factors: customers related to the customer’s willingness to pay demand value, customer loyalty factor, and customer cost factor. Then, the variance contribution rate of the three factors to all observable variables is weighted and converted into the net customer relationship value, at the same time, the embedded microprocessor evolved from the CPU in the general-purpose computer. Its characteristic is that it has a 32-bit or more processor and has high performance. In actual embedded applications, it only retains closeness to embedded applications. Related functional hardware, removing other redundant functional parts so that the special requirements of embedded applications can be realized with the lowest power consumption and resources. Before constructing the structural equation model, it is necessary to test the reliability and validity of the sample data. The data selected in this paper are panel data. In order to judge whether the data is stable, the panel unit root test should be carried out. This article chooses LLC to verify the stability of the panel data. Individual fixed effects and linear time trends are added to the test, and the lag order of the difference lag is 0. Among the intermediary business income of joint-stock commercial banks, bank card fee income accounts for the largest proportion, with China Everbright Bank accounting for 70.12% and China CITIC Bank accounting for 54.16%. The research results show that with the continuous increase of Internet penetration rate, the impact of corporate finance on commercial banks shows phased characteristics. The corporate financial risk measurement method studied in this paper can provide effective help for risk monitoring and early warning. Risk measurement is a management activity that uses certain methods to determine and quantify the possibility of a company’s future financial results deviating from expected financial goals due to various uncertain factors. In practice, it is generally analyzed by a combination of qualitative and quantitative, subjective, and objective methods.

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Analysis of Beijing Tianjin Hebei regional credit system from the perspective of big data credit reporting

Cited by 12 publications

References 22 publications

Cross‐regional comparative study on digital finance and finance efficiency in China: The eastern and non‐eastern areas

Cross‐regional comparative study on digital finance and finance efficiency in China: The eastern and non‐eastern areas

Detection of fintech P2P lending issues in Indonesia

Enterprise Financial Asset Risk Measurement Based on Embedded Microprocessor Security Analysis

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