Big data analytics on enterprise credit risk evaluation of e-Business platform

Wang, Fatao; Ding, Lihui; Yu, Hongxin; Zhao, Yuanjun

doi:10.1007/s10257-019-00414-x

Cited by 66 publications

(39 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the rapid development of information technology, the transmission and management of information has become more complex and time-sensitive, and enterprises and government departments have been implementing various information management systems to enhance operational efficiency and organizational effectiveness (Banker and Kauffman, 2004 ). Cloud financial accounting informatization is a product of the combination of accounting work and information technology, and it is also a current demand for financial information management in the new era, and a necessary means for enterprises to enhance their core competitiveness (Wang et al, 2019 ). With the gradual popularization of big data technology, on the one hand, it provides new development opportunities for cloud financial accounting informatization, and on the other hand, it also brings information security issues.…”

Section: Introductionmentioning

confidence: 99%

“…With the gradual popularization of big data technology, on the one hand, it provides new development opportunities for cloud financial accounting informatization, and on the other hand, it also brings information security issues. Enterprises need to sustainably explore the potential risk prevention for cloud financial ac-counting informatization in the era of big data to promote the stable and comprehensive development of enterprise financial accounting informatization (Wang et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluating the Impact of Information System Quality on Continuance Intention Toward Cloud Financial Information System

Wang

2021

Front. Psychol.

View full text Add to dashboard Cite

Cloud financial accounting informatization is a product of the combination of accounting work and information technology, a current demand for financial information management in the new era, and a necessary means for enterprises to enhance their core competitiveness. Therefore, this study is based on DeLone and McLean's information system success model, and through theoretical interpretation and derivation, we integrate satisfaction and trust in relationship quality to measure the impact of users' intention to continue using the cloud financial accounting system. A sample of 289 faculty and finance staff with experience in using cloud financial accounting systems was used to test the hypotheses using Partial Least Squares (PLS). The results of the study showed that (1) user participation had a significant positive impact on satisfaction with the system quality, information quality, and service quality of the cloud financial accounting system; (2) the system quality and service quality of the cloud financial accounting system had a significant positive effect on user trust; and (3) the quality of the cloud financial accounting system had a mediating effect on intention to continue using the system through satisfaction and trust. Since there is a lack of research on the antecedents and outcomes of user linkage to cloud financial accounting systems in the literature, especially the empirical results on the mediation relationship from the perspective of relationship quality. Therefore, this study can fill above mentioned research gap and provide specific recommendations for sustainable management practice.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluating the Impact of Information System Quality on Continuance Intention Toward Cloud Financial Information System

Wang

2021

Front. Psychol.

View full text Add to dashboard Cite

show abstract

“…As the number of selected lines increases, the current same price for all passengers in different riding paths could make the bus industry development a step further [13]. In terms of default probability of applicants, incomplete market-oriented interest rate has more significant prediction effect, but the use of personal public information can also reflect the default risk to a certain extent [14]. Taking Renrendai as an example, its credit certification mechanism has certain advantages in reflecting credit risk, but the index system has certain limitations, so it is necessary to supplement and improve its evaluation index system.…”

Section: Introductionmentioning

confidence: 99%

Big Data Analytics for Complex Credit Risk Assessment of Network Lending Based on SMOTE Algorithm

Niu

Cai

2020

Complexity

View full text Add to dashboard Cite

With the continuous development of big data technology, the data of online lending platform witness explosive development. How to give full play to the advantages of data, establish a credit risk assessment model, and realize the effective control of platform credit risk have become the focus of online lending platform. In view of the fact that the network loan data are mainly unbalanced data, the smote algorithm is helpful to optimize the model and improve the evaluation performance of the model. Relevant research shows that stochastic forest model has higher applicability in credit risk assessment, and cart, ANN, C4.5, and other algorithms are also widely used. In the influencing factors of credit evaluation, the weight of the applicant’s enterprise scale, working years, historical records, credit score, and other indicators is relatively high, while the index weight of marriage and housing/car production (loan) is relatively low.

show abstract

“…(2) Individual tness evaluation. First of all, through comparative analysis, to understand the tness of different individuals, so as to determine the individuals to be selected into the next generation, and gradually complete the construction of the next generation group; secondly, the algorithm designer needs to determine the tness function of this paper according to the genetic selection needs, and use this as a tool to complete the local guidance search; nally, according to the tness function determined previously Number, analyze the differences of different individuals, and evaluate their tness [15]. Generally speaking, the nature of the problem to be solved is different, and the eld is different.…”

Section: Running Process Of Genetic Algorithmmentioning

confidence: 99%

Research on Investment Portfolio Model Based on Neural Network and Genetic Algorithm

Wei

Zhao

Chen

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

With the maturity of neural network theory, it provides new ideas and methods for the prediction and analysis of stock market investment. The purpose of this paper is to improve the accuracy of stock market investment prediction, we build neural network model and genetic algorithm model, study the law of stock market operation, and improve the effectiveness of neural network and genetic algorithm. Through the empirical research, it is found that the neural network model can make up for the shortcomings of the traditional algorithm through the optimization of genetic algorithm.

show abstract

Big data analytics on enterprise credit risk evaluation of e-Business platform

Cited by 66 publications

References 32 publications

Evaluating the Impact of Information System Quality on Continuance Intention Toward Cloud Financial Information System

Evaluating the Impact of Information System Quality on Continuance Intention Toward Cloud Financial Information System

Big Data Analytics for Complex Credit Risk Assessment of Network Lending Based on SMOTE Algorithm

Research on Investment Portfolio Model Based on Neural Network and Genetic Algorithm

Contact Info

Product

Resources

About