Financial risk assessment model based on big data

2019 ◽  
Vol 10 (04) ◽  
pp. 1950021 ◽  
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.

Using big data to retrospectively validate the COMPASS-CAT risk assessment model: considerations on methodology

2020 ◽  
Author(s):  
Ilias Nikolakopoulos ◽  
Soheila Nourabadi ◽  
Joanna B. Eldredge ◽  
Lalitha Anand ◽  
Meng Zhang ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Big Data ◽  
Assessment Model ◽  
Risk Assessment Model

scholarly journals A novel financial risk assessment model for companies based on heterogeneous information and aggregated historical data

PLoS ONE ◽  
2018 ◽  
Vol 13 (12) ◽  
pp. e0208166 ◽  
Author(s):  
Dan-Ping Li ◽  
Si-Jie Cheng ◽  
Peng-Fei Cheng ◽  
Jian-Qiang Wang ◽  
Hong-Yu Zhang
Keyword(s):  
Risk Assessment ◽  
Financial Risk ◽  
Historical Data ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Heterogeneous Information

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

Complexity ◽  
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.

scholarly journals A Privacy Risk Assessment Model for Medical Big Data Based on Adaptive Neuro-Fuzzy Theory

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Mingyue Shi ◽  
Rong Jiang ◽  
Wei Zhou ◽  
Sen Liu ◽  
Savio Sciancalepore
Keyword(s):  
Risk Assessment ◽  
Big Data ◽  
Privacy Protection ◽  
Internet Security ◽  
Assessment Model ◽  
Security And Privacy ◽  
Risk Assessment Model ◽  
Experimental Process ◽  
Data Environment ◽  
Medical Big Data

Information leakage in the medical industry has become an urgent problem to be solved in the field of Internet security. However, due to the need for automated or semiautomated authorization management for privacy protection in the big data environment, the traditional privacy protection model cannot adapt to this complex open environment. Although some scholars have studied the risk assessment model of privacy disclosure in the medical big data environment, it is still in the initial stage of exploration. This paper analyzes the key indicators that affect medical big data security and privacy leakage, including user access behavior and trust, from the perspective of users through literature review and expert consultation. Also, based on the user’s historical access information and interaction records, the user’s access behavior and trust are quantified with the help of information entropy and probability, and a definition expression is given explicitly. Finally, the entire experimental process and specific operations are introduced in three aspects: the experimental environment, the experimental data, and the experimental process, and then, the predicted results of the model are compared with the actual output through the 10-fold cross verification with Matlab. The results prove that the model in this paper is feasible. In addition, the method in this paper is compared with the current more classical medical big data risk assessment model, and the results show that when the proportion of illegal users is less than 15%, the model in this paper is more superior in terms of accuracy and recall.

Sign in / Sign up

Export Citation Format

Share Document