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MSEs Credit Risk Assessment Model Based on Federated Learning and Feature Selection 被引量:1
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作者 Zhanyang Xu Jianchun Cheng +2 位作者 Luofei Cheng Xiaolong Xu Muhammad Bilal 《Computers, Materials & Continua》 SCIE EI 2023年第6期5573-5595,共23页
Federated learning has been used extensively in business inno-vation scenarios in various industries.This research adopts the federated learning approach for the first time to address the issue of bank-enterprise info... Federated learning has been used extensively in business inno-vation scenarios in various industries.This research adopts the federated learning approach for the first time to address the issue of bank-enterprise information asymmetry in the credit assessment scenario.First,this research designs a credit risk assessment model based on federated learning and feature selection for micro and small enterprises(MSEs)using multi-dimensional enterprise data and multi-perspective enterprise information.The proposed model includes four main processes:namely encrypted entity alignment,hybrid feature selection,secure multi-party computation,and global model updating.Secondly,a two-step feature selection algorithm based on wrapper and filter is designed to construct the optimal feature set in multi-source heterogeneous data,which can provide excellent accuracy and interpretability.In addition,a local update screening strategy is proposed to select trustworthy model parameters for aggregation each time to ensure the quality of the global model.The results of the study show that the model error rate is reduced by 6.22%and the recall rate is improved by 11.03%compared to the algorithms commonly used in credit risk research,significantly improving the ability to identify defaulters.Finally,the business operations of commercial banks are used to confirm the potential of the proposed model for real-world implementation. 展开更多
关键词 Federated learning feature selection credit risk assessment MSEs
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Prioritizing real estate enterprises based on credit risk assessment:an integrated multi‑criteria group decision support framework 被引量:1
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作者 Zhen‑Song Chen Jia Zhou +5 位作者 Chen‑Ye Zhu Zhu‑Jun Wang Sheng‑Hua Xiong Rosa M.Rodríguez Luis Martínez Mirosław J.Skibniewski 《Financial Innovation》 2023年第1期2939-2991,共53页
Credit risk assessment involves conducting a fair review and evaluation of an assessed subject’s solvency and creditworthiness.In the context of real estate enterprises,credit risk assessment provides a basis for ban... Credit risk assessment involves conducting a fair review and evaluation of an assessed subject’s solvency and creditworthiness.In the context of real estate enterprises,credit risk assessment provides a basis for banks and other financial institutions to choose suitable investment objects.Additionally,it encourages real estate enterprises to abide by market norms and provide reliable information for the standardized management of the real estate industry.However,Chinese real estate companies are hesitant to disclose their actual operating data due to privacy concerns,making subjective evalu-ation approaches inevitable,occupying important roles in accomplishing Chinese real estate enterprise credit risk assessment tasks.To improve the normative and reliability of credit risk assessment for Chinese real estate enterprises,this study proposes an integrated multi-criteria group decision-making approach.First,a credit risk assessment index for Chinese real estate enterprises is established.Then,the proposed framework combines proportional hesitant fuzzy linguistic term sets and preference ranking organization method for enrichment evaluation II methods.This approach is suitable for processing large amounts of data with high uncertainty,which is often the case in credit risk assessment tasks of Chinese real estate enterprises involving massive subjec-tive evaluation information.Finally,the proposed model is validated through a case study accompanied by sensitivity and comparative analyses to verify its rationality and feasibility.This study contributes to the research on credit assessment for Chinese real estate enterprises and provides a revised paradigm for real estate enterprise credit risk assessment. 展开更多
关键词 Real estate enterprise credit risk assessment PROMETHEE II Best–worst method Proportional hesitant fuzzy linguistic term sets
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Credit Assessment of Contractors:A Rough Set Method 被引量:2
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作者 刘高军 朱嬿 《Tsinghua Science and Technology》 SCIE EI CAS 2006年第3期357-362,共6页
A rough set method is presented in this paper to assess the credit of contractors. Unlike traditional methods, the rough set method deduces credit-classifying rules from actual data to predict new cases. The method us... A rough set method is presented in this paper to assess the credit of contractors. Unlike traditional methods, the rough set method deduces credit-classifying rules from actual data to predict new cases. The method uses a contractors' database with a genetic algorithm and an exhaustive reduction implemented using ROSETTA software that integrates rough set method. The classification accuracy of the rough set model is not as good as that of a decision tree, logistic regression, and neural network models, but the rough set model more accurately predicts contractors with bad credit. The results show that the rough set model is especially useful for detecting corporations with bad credit in the currently disordered Chinese construction market. 展开更多
关键词 credit assessment rough set data mining construction industry China
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A credit risk assessment model based on SVM for small and medium enterprises in supply chain finance 被引量:22
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作者 Lang Zhang Haiqing Hu Dan Zhang 《Financial Innovation》 2015年第1期208-228,共21页
Background:Supply chain finance(SCF)is a series of financial solutions provided by financial institutions to suppliers and customers facing demands on their working capital.As a systematic arrangement,SCF utilizes the... Background:Supply chain finance(SCF)is a series of financial solutions provided by financial institutions to suppliers and customers facing demands on their working capital.As a systematic arrangement,SCF utilizes the authenticity of the trade between(SMEs)and their“counterparties”,which are usually the leading enterprises in their supply chains.Because in these arrangements the leading enterprises are the guarantors for the SMEs,the credit levels of such counterparties are becoming important factors of concern to financial institutions’risk management(i.e.,commercial banks offering SCF services).Thus,these institutions need to assess the credit risks of the SMEs from a view of the supply chain,rather than only assessing an SME’s repayment ability.The aim of this paper is to research credit risk assessment models for SCF.Methods:We establish an index system for credit risk assessment,adopting a view of the supply chain that considers the leading enterprise’s credit status and the relationships developed in the supply chain.Furthermore,We conducted two credit risk assessment models based on support vector machine(SVM)technique and BP neural network respectly.Results:(1)The SCF credit risk assessment index system designed in this paper,which contained supply chain leading enterprise’s credit status and cooperative relationships between SMEs and leading enterprises,can help banks to raise their accuracy on predicting a small and medium enterprise whether default or not.Therefore,more SMEs can obtain loans from banks through SCF.(2)The SCF credit risk assessment model based on SVM is of good generalization ability and robustness,which is more effective than BP neural network assessment model.Hence,Banks can raise the accuracy of credit risk assessment on SMEs by applying the SVM model,which can alleviate credit rationing on SMEs.Conclusions:(1)The SCF credit risk assessment index system can solve the problem of banks incorrectly labeling a creditworthy enterprise as a default enterprise,and thereby improve the credit rating status in the process of SME financing.(2)By analyzing and comparing the empirical results,we find that the SVM assessment model,on evaluating the SME credit risk,is more effective than the BP neural network assessment model.This new assessment model based on SVM can raise the accuracy of classification between good credit and bad credit SMEs.(3)Therefore,the SCF credit risk assessment index system and the assessment model based on SVM,is the optimal combination for commercial banks to use to evaluate SMEs’credit risk. 展开更多
关键词 SCF SMES credit risk assessment SVM BP Neural Network Technique
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The Credit Risk Assessment Model of Internet Supply Chain Finance: Multi-Criteria Decision-Making Model with the Principle of Variable Weight 被引量:1
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作者 Yueliang Su Baoyu Zhong 《Journal of Computer and Communications》 2016年第16期1-11,共11页
The characteristics of the financing model are firstly analyzed when the e-commerce enterprises participate in the supply chain finance. Internet supply chain finance models are divided into three categories with the ... The characteristics of the financing model are firstly analyzed when the e-commerce enterprises participate in the supply chain finance. Internet supply chain finance models are divided into three categories with the standard of whether the Electronic commerce enterprises provide funds for small and medium enterprises instead of banks. And then we further study the financing process and the functions of the e-commerce platform with specific examples. Finally, combined with the characteristics of the supply chain finance model, we set up a small and medium enterprises credit evaluation model based on the principle of variable weight with its dynamic data. At the same time, a multi time points and multi indicators decision-making method based on the principle of variable weight is proposed and a specific example is presented. In this paper, the Multi-criteria decision-making model with the principle of variable weight has been used two times. At last, a typical case has been analyzed based on this model with a higher accuracy rate of credit risk assessment. 展开更多
关键词 credit Risk assessment Model Multi-Criteria Decision-Making Model Variable Principle
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Personal Credit Risk .Scoring Model Based on Rough Set and Neural Network
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作者 Hui Lu Shangfeng Yao 《Journal of Systems Science and Information》 2008年第4期307-314,共8页
In order to improve the precision of personal credit risk assessment, applying rough set and neural network to the credit risk scoring prediction problem in an attempt to suggest a new model with better classification... In order to improve the precision of personal credit risk assessment, applying rough set and neural network to the credit risk scoring prediction problem in an attempt to suggest a new model with better classification accuracy. To evaluate the prediction accuracy of the model, we compare its performance with those of SVM, linear discriminate analysis, logistic regression analysis, K-nearest neighbors, classification and regression tree, neural network and PCA-NN. The experimental results show the model have a very good prediction accuracy 展开更多
关键词 credit risk credit risk assessment rough set neural network 5-fold cross-validation
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