Network Financial Fraud Risk Assessment System Based on Big Data Analysis

In recent years, network technology has continued to develop, and Internet finance has rapidly developed into a new business area. Internet credit is one of the important ways for banks to conduct business, and the scale of online credit has continued to expand. Due to the existence of various unpredictable factors, frequent emergencies, and online financial fraud, the overall market risk in the field of online credit has increased, and the rate of non-performing loans has continued to increase. Online financial fraud cases show that online credit risk has become one of the most prominent risks in the operation of commercial banks, which has a direct impact on the stability and development of commercial banks. We can build a bank database system based on big data, introduce professional big data analysis technical personnel, and constantly improve the big data sharing analysis platform, so that commercial banks can use system data more fully and effectively, and facilitate relevant business personnel to use big data technology for analysis and calculation. Big data is constantly produced, which provides basic materials for online credit risk assessment. Big data analysis technology is gradually mature, and it has the necessary conditions for online credit risk assessment. Based on the theories and technologies related to big data analysis, this paper comprehensively evaluates the online credit risk in the form of example data analysis, thereby effectively reducing the online credit risk coefficient.

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A Risk Assessment Algorithm for College Student Entrepreneurship Based on Big Data Analysis

Complexity ◽

10.1155/2021/6359296 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Chengjun Zhou ◽

DuanXu Wang

Keyword(s):

Risk Factors ◽

Risk Assessment ◽

Big Data ◽

Data Analysis ◽

College Student ◽

Big Data Analysis ◽

Technical Support ◽

Assessment Process ◽

Student Entrepreneurship ◽

Study Results

College student entrepreneurship is a complex and dynamic process, in which the potential risks faced by entrepreneurial enterprises are interactive and diverse. The changes in risk assessment for college student entrepreneurship are also dynamic and nonlinear and are affected by many factors, which make the risk assessment process for college student entrepreneurship quite complicated. Big data analysis technology is a new product formed under the background of cloud computing and Internet technology, which has the characteristics of large data scale, multiple data types, and strong data value and provides more technical support for the researches on the risk assessment algorithm for college student entrepreneurship. On the basis of summarizing and analyzing previous research results, this article expounded the research status and significance of the risk assessment algorithm for college student entrepreneurship, elaborated the development background, current status, and future challenges of big data analysis technology, introduced the basic principles of support vector machine (SVM) and hierarchical analytic process, constructed a risk assessment model for college student entrepreneurship based on big data analysis, analyzed the risk factors and assessment indicators of the entrepreneurial model, proposed a risk assessment algorithm for college student entrepreneurship based on big data analysis, performed the discrimination coefficient calculation and comprehensive correlation optimization, and finally conducted a case experiment and its result analysis. The study results show that the risk assessment algorithm for college student entrepreneurship based on big data analysis can effectively realize the comprehensive management of risk factors, make full use of the value of assessment parameter data, and significantly improve the accuracy and efficiency of the risk assessment for college student entrepreneurship, providing more technical support for the researches on the risk assessment algorithm for college student entrepreneurship. The study results of this article provide a reference for further researches on the risk assessment algorithm of college student entrepreneurship based on big data analysis.

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A Big Data Analysis Approach for Rail Failure Risk Assessment

Risk Analysis ◽

10.1111/risa.12836 ◽

2017 ◽

Vol 37 (8) ◽

pp. 1495-1507 ◽

Cited By ~ 36

Author(s):

Ali Jamshidi ◽

Shahrzad Faghih-Roohi ◽

Siamak Hajizadeh ◽

Alfredo Núñez ◽

Robert Babuska ◽

...

Keyword(s):

Risk Assessment ◽

Big Data ◽

Data Analysis ◽

Big Data Analysis ◽

Analysis Approach ◽

Failure Risk

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Risk Assessment Model of College Students' New Ventures Based on Big Data Analysis and Fuzzy Evaluation

2019 12th International Conference on Intelligent Computation Technology and Automation (ICICTA) ◽

10.1109/icicta49267.2019.00125 ◽

2019 ◽

Author(s):

Yi Zheng

Keyword(s):

College Students ◽

Risk Assessment ◽

Big Data ◽

Data Analysis ◽

New Ventures ◽

Big Data Analysis ◽

Assessment Model ◽

Fuzzy Evaluation ◽

Risk Assessment Model

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Health Assessment System Based on Big Data Analysis of Meridian Electrical Potential

Proceedings of the 3rd International Conference on Biomedical Signal and Image Processing - ICBIP '18 ◽

10.1145/3278229.3278235 ◽

2018 ◽

Author(s):

Hui Li ◽

Yibo Cheng ◽

Yinghui Li ◽

Xiaochang Ma ◽

Delong Li

Keyword(s):

Big Data ◽

Data Analysis ◽

Electrical Potential ◽

Health Assessment ◽

Big Data Analysis ◽

Assessment System

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Financial risk assessment model based on big data

International Journal of Modeling Simulation and Scientific Computing ◽

10.1142/s1793962319500211 ◽

2019 ◽

Vol 10 (04) ◽

pp. 1950021 ◽

Cited By ~ 3

Author(s):

Qiong Kang

Keyword(s):

Risk Assessment ◽

Big Data ◽

Regression Analysis ◽

Data Analysis ◽

Financial Risk ◽

Big Data Analysis ◽

Assessment Model ◽

Risk Assessment Model ◽

Analysis Method ◽

Variable Model

Conventional financial risk assessment is not accurate and its adaptive assessment ability is low. In order to solve this problem, a financial risk assessment model based on big data is proposed. In this method, the quantitative analysis method is adopted to analyze the explanatory variable model and the control variable model of financial risk assessment. The market-to-book ratio, asset–liability ratio, cash flow ratio and financing structure model are adopted as constraint parameters to construct a big data analysis model for financial risk assessment. On this basis, the adaptive fuzzy weighted control method is adopted for information fusion of financial risk assessment data and big data classification, and the asset income control and innovative evaluation model are adopted for linear planning and square fitting during financial risk assessment. Based on the intervention factors of financial market participants, quantitative regression analysis is performed, and according to the economic game theory, big data analysis and prediction of financial risk assessment are performed through the regression analysis method. Then the big data fusion and clustering algorithms are adopted for financial risk assessment. The simulation results show that this method can provide a relatively high accuracy in financial risk assessment, and has relatively strong adaptive evaluation capability to the risk coefficient, so it has a good application value in the prevention and control of risk factors in financial systems.

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