Application of Data Mining Combined with Fuzzy Multicriteria Decision-Making in Credit Risk Assessment from Legal Service Companies

The purpose is to avert the systematic financial risks from the Internet financial bubble and improve the efficiency of legal service companies’ credit risk assessment ability. Firstly, this study analyzes the commonly used classification model, Support Vector Machine (SVM), and linear regression mo...

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Autores principales:	Jianmiao Hu, Chong Chen, Kongze Zhu
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/fec015fec65b4b4285093b1b852aeb2e
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spelling	oai:doaj.org-article:fec015fec65b4b4285093b1b852aeb2e2021-11-29T00:56:22ZApplication of Data Mining Combined with Fuzzy Multicriteria Decision-Making in Credit Risk Assessment from Legal Service Companies1563-514710.1155/2021/2499948https://doaj.org/article/fec015fec65b4b4285093b1b852aeb2e2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/2499948https://doaj.org/toc/1563-5147The purpose is to avert the systematic financial risks from the Internet financial bubble and improve the efficiency of legal service companies’ credit risk assessment ability. Firstly, this study analyzes the commonly used classification model, Support Vector Machine (SVM), and linear regression model, Logistic model, and then puts forward the integrated SVM-Logistic + Fuzzy Multicriteria Decision-Making (FMCDM) to evaluate and analyze the credit risk level of listed companies. In the proposed integrated model, the SVM model classifies the data sample from listed companies, and the Logistic model is used for regression analysis on the credit risk assessment. Based on the credit risk indexes and weight uncertain factors of sample companies, FMCDM based on fuzzy set is applied to obtain the evaluation indexes. Then, the Analytic Hierarchy Process (AHP) is used to obtain the weight of key indexes. Finally, the fit analysis is carried out according to the existing risk status of the sample company and the risk status results of the proposed integrated model. The results show that the integrated SVM-Logistic model is complementary and has high intensive evaluation. According to the fitness value obtained by FMCDM, the company's credit risk status can be accurately evaluated, and the intermediate threshold of corporate credit default risk measurement is 0.56152; if Fit is lower than the threshold, the company’s credit is low, and if Fit is higher than the threshold, the company’s credit is high. Therefore, the data mining technology based on integrated SVM-Logistic model + FMCDM has high precision and feasible application in the credit risk assessment from legal service companies. This study creates a new method model for legal service companies in the field of corporate credit risk assessment and can provide references and ideas for corporate credit risk assessment.Jianmiao HuChong ChenKongze ZhuHindawi LimitedarticleEngineering (General). Civil engineering (General)TA1-2040MathematicsQA1-939ENMathematical Problems in Engineering, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
spellingShingle	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939 Jianmiao Hu Chong Chen Kongze Zhu Application of Data Mining Combined with Fuzzy Multicriteria Decision-Making in Credit Risk Assessment from Legal Service Companies
description	The purpose is to avert the systematic financial risks from the Internet financial bubble and improve the efficiency of legal service companies’ credit risk assessment ability. Firstly, this study analyzes the commonly used classification model, Support Vector Machine (SVM), and linear regression model, Logistic model, and then puts forward the integrated SVM-Logistic + Fuzzy Multicriteria Decision-Making (FMCDM) to evaluate and analyze the credit risk level of listed companies. In the proposed integrated model, the SVM model classifies the data sample from listed companies, and the Logistic model is used for regression analysis on the credit risk assessment. Based on the credit risk indexes and weight uncertain factors of sample companies, FMCDM based on fuzzy set is applied to obtain the evaluation indexes. Then, the Analytic Hierarchy Process (AHP) is used to obtain the weight of key indexes. Finally, the fit analysis is carried out according to the existing risk status of the sample company and the risk status results of the proposed integrated model. The results show that the integrated SVM-Logistic model is complementary and has high intensive evaluation. According to the fitness value obtained by FMCDM, the company's credit risk status can be accurately evaluated, and the intermediate threshold of corporate credit default risk measurement is 0.56152; if Fit is lower than the threshold, the company’s credit is low, and if Fit is higher than the threshold, the company’s credit is high. Therefore, the data mining technology based on integrated SVM-Logistic model + FMCDM has high precision and feasible application in the credit risk assessment from legal service companies. This study creates a new method model for legal service companies in the field of corporate credit risk assessment and can provide references and ideas for corporate credit risk assessment.
format	article
author	Jianmiao Hu Chong Chen Kongze Zhu
author_facet	Jianmiao Hu Chong Chen Kongze Zhu
author_sort	Jianmiao Hu
title	Application of Data Mining Combined with Fuzzy Multicriteria Decision-Making in Credit Risk Assessment from Legal Service Companies
title_short	Application of Data Mining Combined with Fuzzy Multicriteria Decision-Making in Credit Risk Assessment from Legal Service Companies
title_full	Application of Data Mining Combined with Fuzzy Multicriteria Decision-Making in Credit Risk Assessment from Legal Service Companies
title_fullStr	Application of Data Mining Combined with Fuzzy Multicriteria Decision-Making in Credit Risk Assessment from Legal Service Companies
title_full_unstemmed	Application of Data Mining Combined with Fuzzy Multicriteria Decision-Making in Credit Risk Assessment from Legal Service Companies
title_sort	application of data mining combined with fuzzy multicriteria decision-making in credit risk assessment from legal service companies
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/fec015fec65b4b4285093b1b852aeb2e
work_keys_str_mv	AT jianmiaohu applicationofdataminingcombinedwithfuzzymulticriteriadecisionmakingincreditriskassessmentfromlegalservicecompanies AT chongchen applicationofdataminingcombinedwithfuzzymulticriteriadecisionmakingincreditriskassessmentfromlegalservicecompanies AT kongzezhu applicationofdataminingcombinedwithfuzzymulticriteriadecisionmakingincreditriskassessmentfromlegalservicecompanies
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Application of Data Mining Combined with Fuzzy Multicriteria Decision-Making in Credit Risk Assessment from Legal Service Companies

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