Big data driven Internet of Things for credit evaluation and early warning in finance

Chen, Wen; Yang, Jinhai; Gan, Liu; Pan, Yang

doi:10.1016/j.future.2021.06.003

Cited by 46 publications

(23 citation statements)

References 25 publications

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“…It can be seen that the errors are less than 10%, which meets the prediction requirements, as shown in Figure 4(a) [23,24]. In the analysis of SVM prediction model, the first eight influence factors are used as the input vector, and the ninth influence factor is used as the output vector to establish the SVM model, and the simulation test is carried out on MATLAB [9,25]. e Gaussian radial kernel function is selected as the kernel function predicted in this study, and the parameters of SVM are selected by cross validation.…”

Section: Experimental Design and Analysismentioning

confidence: 74%

“…Wen C. and others use distributed search engine technology to customize the web crawler to obtain the required bank card and transaction data from the multisource heterogeneous data of the Internet of ings financial industry, design the corresponding spark parallel algorithm to preprocess the data, and establish the inverted table and secondary index document, which provides a data source for the big data analysis platform. e results show that this method can significantly reduce the probability of banks formulating the first and second error rates and effectively reduce the losses caused by improper credit regulations when evaluating the credit risk of Internet of ings financial financing [9]. Genetic support vector machine algorithm (GA-SVM) has been highly valued by many relevant professionals.…”

Section: Related Workmentioning

confidence: 99%

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Construction of Enterprise Financial Information Intelligent Processing Innovation Model Based on Internet of Things Technology

Ding

2022

Computational Intelligence and Neuroscience

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The application of Internet of Things technology provides conditions for the systematization of enterprise financial data and the intellectualization of financial management. In this study, support vector machine (SVM) algorithm and genetic algorithm (GA) are combined to obtain the innovation model of enterprise financial information intelligent processing based on GA-SVM optimization algorithm. Nine factors affecting enterprise financial information processing from 2010 to 2018 are selected as influencing factors, and according to the idea and method of data modeling, the simulation experiment of enterprise financial information intelligent processing model is carried out with MATLAB. The results show that the average fitness of GA-SVM is close to the best fitness, indicating that each individual in the population is near the optimal solution. The RMSE of GA-SVM is 0.35%, and the root mean square error obtained is greater than this value, which shows that the application effect of GA-SVM is significantly better than that of SVM using cross validation method, and it is more suitable for precision prediction. At the same time, it also reflects that the optimization of SVM parameters by GA is reliable. The algorithm can be better applied in the intelligent processing of enterprise financial information and can provide a certain reference for enterprises in the financial management based on the Internet of Things.

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Section: Experimental Design and Analysismentioning

confidence: 74%

Section: Related Workmentioning

confidence: 99%

Construction of Enterprise Financial Information Intelligent Processing Innovation Model Based on Internet of Things Technology

Ding

2022

Computational Intelligence and Neuroscience

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“…e third is operational risk. High risks are usually caused by human factors, such as employee negligence at work, leading to certain changes in the company and possible losses, or deliberately using certain methods to invade illegally or using commercial systems to gain advantages [41]. On the one hand, these problems are caused by the imperfect operating rules and regulations of Internet finance companies and weak supervision.…”

Section: Research On Internet Financial Risk Managementmentioning

confidence: 99%

Internet Financial Risk Management in the Context of Big Data and Artificial Intelligence

Wang

2022

Mathematical Problems in Engineering

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In recent years, the emergence of big data and artificial intelligence technology has made Internet finance a brand new development model in the new era. As an emerging financial format, Internet finance plays an important role in providing people with convenient and efficient services. However, due to the late start in this regard and the imperfect related policies and regulations, China is currently still in the development stage, resulting in its risk management system not being mature and complete and lacking uniformity. There are also many regulatory deficiencies, which are not conducive to the healthy, stable, and continuous growth and progress of Internet finance. In the new situation, it is of great significance to strengthen the research on the security of China's Internet finance. Therefore, how to effectively manage Internet financial risks in the context of big data and artificial intelligence has become a topic of research. This study uses questionnaire analysis and data analysis to understand the distribution of risks and the importance of risk response measures through questionnaire surveys. According to the survey results, in the eyes of most interviewees, the ratios of operational risk, credit risk, platform operation risk, and lack of law and reputation risk in high-risk areas are 0.15, 0.3, 0.29, 0.51, and 0.1, respectively. The risks of these first-level indicators need to be particularly important and need to be effectively avoided to manage Internet financial risks. In addition, the most important risk response measures are the construction of information security, followed by the improvement of relevant laws and regulations. In their view, only from these aspects can we effectively control risks internally and externally.

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“…The basic motivation behind grid computing is complex problems solving and powerful resource sharing. The authors in [11] have provided comprehensive survey on grid computing systems. Cloud computing is another dimension of distributed computing systems which facilitate the users by providing three basic services: (1) software, (2) platform, and (3) infrastructre.…”

Section: Introductionmentioning

confidence: 99%

Energy Efficient Real-time Tasks Scheduling on High Performance Edge- Computing Systems using Genetic Algorithm

Hussain

Qureshi²,

Khan³

et al. 2023

Preprint

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With increase in number of processing cores or systems, the high-performance edge-computing system’s power and computation speed will increase essentially. However, this comes at the expense of high-energy utilization. One notable solution to reduce the energy consumption of these system is to execute these systems at the slowest feasible speed so that the job’s deadline times are met. Unfortunately, this method is at the expense of more response time and performance loss. To resolve this issue, in this paper we propose a GA-FiFeS approach that associates genetic algorithm with the first feasible speed (FiFeS) approach. This does not jeopardize real-time task deadlines. The GA-FiFeS proposes an energy-efficient schedule while still ensuring high response times. The results of the proposed approach, using plausible assumptions, are compared with currently in practice approaches i.e. FiFeS and LeFeS. As far as energy usage and response times are concerned, the GA-FiFeSS approach surpasses the FiFeS and LeFeS strategies, respectively.

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Big data driven Internet of Things for credit evaluation and early warning in finance

Cited by 46 publications

References 25 publications

Construction of Enterprise Financial Information Intelligent Processing Innovation Model Based on Internet of Things Technology

Construction of Enterprise Financial Information Intelligent Processing Innovation Model Based on Internet of Things Technology

Internet Financial Risk Management in the Context of Big Data and Artificial Intelligence

Energy Efficient Real-time Tasks Scheduling on High Performance Edge- Computing Systems using Genetic Algorithm

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