Research on PPP Enterprise Credit Dynamic Prediction Model

Zhao, Likun; Yang, Shaotang; Wang, Shouqing; Shen, Jianxiong

doi:10.3390/app122010362

Cited by 9 publications

(4 citation statements)

References 30 publications

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“…This helped them stay profitable (Olarewaju and Msomi 2022). However, the crisis revealed the profitability indicators, such as ROA and ROE, that are most affected by the economic downturn (Zhao et al 2022). Numerous mathematical and statistical techniques were used to draw this conclusion.…”

Section: Literature Reviewmentioning

confidence: 99%

Does the Size of the Business Still Matter, or Is Profitability under New Management, by Order of the COVID-19?

Blazek

Ďurana

Michulek

et al. 2023

JRFM

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Businesses should come up with a strategy, plans, and goals so that their total assets can make a profit during the transformation process. Utilizing various features of a property can generate this income. This comparison provides evidence of profitability. During the global economic downturn, a number of businesses encountered issues that caused their payment situations and profitability to deteriorate. The goal of this article is to ascertain whether particular profitability indicators also revealed the pandemic-related global crisis, particularly in the Visegrad Group countries. This analysis was conducted based on categories of business size. Specifically, 8671 enterprises were analyzed. The evaluation of indicators revealed whether there was a significant change in a negative direction, a significant change in a positive direction, or no significant change. It was possible to make a clear diagram of the companies that took part in the study and to figure out the median values in order to compare the results of the chosen profitability indicators. Correspondence analysis was conducted so that conclusions could be more accurate. According to the findings of this study, indicators of ROA, ROE, and ROS did not change significantly across enterprise size categories in the years preceding, during, and after the pandemic. Since the government regulations of the V4 countries had a significant impact on these businesses, the change was most obvious in the case of small businesses within the ROS indicator. The added value of the article is derived from its analysis of selected profitability indicators in the largest group of Central European nations and its relevance.

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Section: Literature Reviewmentioning

confidence: 99%

Does the Size of the Business Still Matter, or Is Profitability under New Management, by Order of the COVID-19?

Blazek

Ďurana

Michulek

et al. 2023

JRFM

View full text Add to dashboard Cite

show abstract

“…In the same manner, there is an expectation of an increase in fraud in the following years [20]. Another problem to consider is the consistency of the information recorded at the different stages of the process, such as sales data [21], cultural variables, environmental data [22], macroeconomics [23], innovation capacity management and development, exchange rate evolution, Gross Domestic Product (GDP) growth trends [23,24], economic activity, and experience [25].…”

Section: Current Researchmentioning

confidence: 99%

Machine Learning for Credit Risk Prediction: A Systematic Literature Review

Noriega,

Rivera,

Herrera

2023

Data

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In this systematic review of the literature on using Machine Learning (ML) for credit risk prediction, we raise the need for financial institutions to use Artificial Intelligence (AI) and ML to assess credit risk, analyzing large volumes of information. We posed research questions about algorithms, metrics, results, datasets, variables, and related limitations in predicting credit risk. In addition, we searched renowned databases responding to them and identified 52 relevant studies within the credit industry of microfinance. Challenges and approaches in credit risk prediction using ML models were identified; we had difficulties with the implemented models such as the black box model, the need for explanatory artificial intelligence, the importance of selecting relevant features, addressing multicollinearity, and the problem of the imbalance in the input data. By answering the inquiries, we identified that the Boosted Category is the most researched family of ML models; the most commonly used metrics for evaluation are Area Under Curve (AUC), Accuracy (ACC), Recall, precision measure F1 (F1), and Precision. Research mainly uses public datasets to compare models, and private ones to generate new knowledge when applied to the real world. The most significant limitation identified is the representativeness of reality, and the variables primarily used in the microcredit industry are data related to the Demographic, Operation, and Payment behavior. This study aims to guide developers of credit risk management tools and software towards the existing ability of ML methods, metrics, and techniques used to forecast it, thereby minimizing possible losses due to default and guiding risk appetite.

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“…Artificial intelligence out of control: In the ChatGPT system, complex machine learning algorithms are used [11] . If the algorithm fails, it may cause serious errors in the system, such as recommending the wrong product to consumers, or misjudging user feedback.…”

Section: Prediction Of the Impact Of Chatgpt On The E-commerce Marketmentioning

confidence: 99%

Research on the Impact of ChatGPT on The Economic Development of E-commerce Industry

Zhang

2023

FBEM

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This article summarizes the impact of the new artificial intelligence technology ChatGPT on the economic development of the e-commerce industry and provides an analysis of two main aspects. Firstly, this paper studies the application mechanism of ChatGPT in the e-commerce industry. By investigating the specific application of ChatGPT on the e-commerce platform, including fine recommendation, intelligent customer service and consumer insight, the potential of ChatGPT in improving the operational efficiency of e-commerce enterprises is revealed. The research results show that ChatGPT can provide personalized services and suggestions, help improve the quality and efficiency of customer service, and reduce the workload of manual customer service. Secondly, we predict the impact of ChatGPT on the future development of the e-commerce industry by mining the characteristics of Amazon's financial data. The results show that ChatGPT is expected to meet these challenges and have a positive impact on the e-commerce market. For example, the introduction of ChatGPT technology may change the competitive landscape of the e-commerce market and increase market concentration ; at the same time, ChatGPT's personalized recommendations and suggestions can improve consumers' shopping satisfaction and purchase rate, and promote the development of the e-commerce industry. In summary, this paper reveals the potential positive impact of ChatGPT on the economic development of the e-commerce industry by analyzing the application mechanism of ChatGPT in the e-commerce industry and predicting its impact on the future development of the e-commerce industry. These research results have important reference value for decision makers and practitioners in the e-commerce industry, and can help them better understand and respond to the challenges and opportunities of new artificial intelligence technologies for the industry.

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Research on PPP Enterprise Credit Dynamic Prediction Model

Cited by 9 publications

References 30 publications

Does the Size of the Business Still Matter, or Is Profitability under New Management, by Order of the COVID-19?

Does the Size of the Business Still Matter, or Is Profitability under New Management, by Order of the COVID-19?

Machine Learning for Credit Risk Prediction: A Systematic Literature Review

Research on the Impact of ChatGPT on The Economic Development of E-commerce Industry

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