Big data analysis of e-commerce loan risk of college students in the context of network finance

Sun, Hong-Mei

doi:10.1007/s10257-019-00424-9

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…They are classified into three sub-classes. The first category talks about finance and stock data (Failed 2018c;Haitao 2020;Chen 2018a;Saito and Gupta 2022;Han et al 2023). And the second ).…”

Section: E-commerce Applicationsmentioning

confidence: 99%

“…They found the adaptive features in short-term, high-risk trading when noisy data, like stock prices, were present and showed their ensemble the performance. In (Haitao 2020), the expanding situation of the targeted e-commerce supply chain management information system was proposed. The evaluation results indicated a large-scale reduction of costs and enhancements in the system's efficiency.…”

Section: Overview Of E-commerce Articlesmentioning

confidence: 99%

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Big data and predictive analytics: A systematic review of applications

Jamarani,

Haddadi,

Sarvizadeh

et al. 2024

Artif Intell Rev

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Big data involves processing vast amounts of data using advanced techniques. Its potential is harnessed for predictive analytics, a sophisticated branch that anticipates unknown future events by discerning patterns observed in historical data. Various techniques obtained from modeling, data mining, statistics, artificial intelligence, and machine learning are employed to analyze available history to extract discriminative patterns for predictors. This study aims to analyze the main research approaches on Big Data Predictive Analytics (BDPA) based on very up-to-date published articles from 2014 to 2023. In this article, we fully concentrate on predictive analytics using big data mining techniques, where we perform a Systematic Literature Review (SLR) by reviewing 109 articles. Based on the application and content of current studies, we introduce taxonomy including seven major categories of industrial, e-commerce, smart healthcare, smart agriculture, smart city, Information and Communications Technologies (ICT), and weather. The benefits and weaknesses of each approach, potentially important changes, and open issues, in addition to future paths, are discussed. The compiled SLR not only extends on BDPA’s strengths, open issues, and future works but also detects the need for optimizing the insufficient metrics in big data applications, such as timeliness, accuracy, and scalability, which would enable organizations to apply big data to shift from retrospective analytics to prospective predictive if fulfilled.

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Section: E-commerce Applicationsmentioning

confidence: 99%

Section: Overview Of E-commerce Articlesmentioning

confidence: 99%

Big data and predictive analytics: A systematic review of applications

Jamarani,

Haddadi,

Sarvizadeh

et al. 2024

Artif Intell Rev

View full text Add to dashboard Cite

show abstract

“…Literature [11] in the examination of ecommerce consumer preferences found that consumers prefer domestic or closer countries, while pointing out that e-commerce transactions risky orders are carefully reviewed to reduce the risk and loss of customers and sellers. Literature [12] examines and analyzes the current e-commerce supply chain system processes and link junction solutions, emphasizing the integration and unification of the supply chain system information, which can improve the efficiency of the entire e-commerce operation and effectively reduce the cost of this information and operational costs, as well as the potential financial risks. Literature [13] explored the effectiveness of focus theory applied to the ecommerce analysis framework, pointing out that it is feasible and effective to use focus theory to make predictions about consumers' consumption preferences.…”

Section: Introductionmentioning

confidence: 99%

Construction and validation of a financial risk prediction model for e-commerce enterprises oriented to financial sharing

Wang

2024

Applied Mathematics and Nonlinear Sciences

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Relying on advanced technology, the financial shared management model realizes a breakthrough for the enhancement of enterprise financial management capability, and provides effective support for enterprise decision makers while mining financial risks. In this paper, the support vector machine algorithm (SVM) is used to construct a financial risk prediction model for e-commerce enterprises, and the radial basis kernel function (RBF) is selected among various kernel functions to enhance the discriminative ability of SVM. At the same time, the particle swarm algorithm (PSO) is introduced to optimize parameter γ and penalty factor C of the RBF kernel function, and the PSO-SVM fusion model is constructed, so as to improve the prediction accuracy of the e-commerce enterprise financial risk prediction model. After the experimental analysis of this paper on the various capabilities of enterprise financial management, it is found that the operating cash flow ratio of the e-commerce enterprise is less than 40% in 2017-2021, and reaches a minimum of 3% in 2020, which indicates that the Z e-commerce enterprise has insufficient ability to raise debt to operate, which may cause problems such as a decrease in the rate of return on assets. In addition, the cash flow of the e-commerce enterprise is highly variable, and the cash outflow from operating activities is significantly higher than the cash inflow in 2020, resulting in a negative net inflow of −111,451,575,000 yuan. It indicates that the Z e-commerce enterprise has a low degree of control over cash flow, and there is a financial risk of a sudden break in the capital chain. Therefore, the risk prediction model constructed in this paper can uncover potential hidden financial dangers and risks by analyzing the financial data of e-commerce enterprises.

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RETRACTED ARTICLE: The Application of Artificial Immune Network in E-Commerce Credit Risk Assessment

Zhang

2023

Int J Comput Intell Syst

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In order to improve the accuracy of e-commerce credit risk assessment, this paper suggests utilizing an artificial immune network to upgrade the text mining algorithm. Through this process, a new e-commerce risk assessment model reliant on the improved algorithm can be constructed with the intention of decreasing the likelihood of risk in digital transactions. The results show that the accuracy and loss rate of the improved clustering algorithm are 97.3% and 4.3%, respectively, both of which are better than the comparison algorithm. Then, the empirical analysis of the e-commerce credit risk assessment model proposed in the study shows that the average fitness and accuracy of the model after stability are 0.0022 and 95.63%, respectively, demonstrating superior performance compared to the comparison model. The above results show that the improved algorithm and the risk assessment model have good performance. Therefore, using this model to evaluate the credit risk of e-commerce can not only improve the accuracy of credit evaluation and promote the sustainable development of e-commerce. Furthermore, it can catalyze the adoption of innovative credit evaluation methods and promote the application of artificial intelligence technology in e-commerce.

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Big data analysis of e-commerce loan risk of college students in the context of network finance

Cited by 3 publications

References 16 publications

Big data and predictive analytics: A systematic review of applications

Big data and predictive analytics: A systematic review of applications

Construction and validation of a financial risk prediction model for e-commerce enterprises oriented to financial sharing

RETRACTED ARTICLE: The Application of Artificial Immune Network in E-Commerce Credit Risk Assessment

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