{"title":"模糊逻辑在银行信用风险评价中的应用","authors":"M. Ozerova, Nicolay Stoletovs, I. Zhigalov","doi":"10.14529/CTCR210207","DOIUrl":null,"url":null,"abstract":"The banking system is a constantly evolving system. The information environment of the bank is growing, the volumes of processed information are increasing due to the growth of users and banking products. To reduce risks, banks make a financial assessment of the situation of individuals and legal entities. The aim of the work is to develop fuzzy multi-connected models designed to predict the receipt of a positive or negative decision to receive a banking product. The decision is made based on scoring. Scoring consists in assigning points for completing a certain questionnaire developed by underwriters of credit risk assessors. Based on the results of the points gained, the system automatically makes a decision on approving or refusing to issue a loan. Different banks have diffe¬rent scoring models. Purpose of the study. The paper considers the use of fuzzy models for making a decision by a bank to issue a banking product that implements the concept of “soft computing”. Methods. The use of fuzzy logic methods in credit scoring is not new, but it is not widely used in practice because it is expensive to integrate into existing systems. Each bank uses its own indicators of the client's financial reliability in scoring. Most of the indicators in banks are the same, but when deciding to issue different banking products, they have different numerical values. The data of the standard scoring methodology of a real bank were taken as the initial data. To predict a bank's decision to issue a banking product to a client, a fuzzy model was applied, production rules were proposed, and membership functions were determined. The model focused on the simultaneous processing of incoming data from multiple clients and for different banks and different scoring models. Results. The developed mathematical model for assessing the client's rating and predicting the decision to receive a banking product based on the fuzzy inference rule. The obtained results are proposed to be used in a multi-banking web-oriented system of providing banking products to corporate clients.","PeriodicalId":338904,"journal":{"name":"Bulletin of the South Ural State University. Ser. Computer Technologies, Automatic Control & Radioelectronics","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application of Fuzzy Logic to Assess Banks' Credit Risk\",\"authors\":\"M. Ozerova, Nicolay Stoletovs, I. Zhigalov\",\"doi\":\"10.14529/CTCR210207\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The banking system is a constantly evolving system. The information environment of the bank is growing, the volumes of processed information are increasing due to the growth of users and banking products. To reduce risks, banks make a financial assessment of the situation of individuals and legal entities. The aim of the work is to develop fuzzy multi-connected models designed to predict the receipt of a positive or negative decision to receive a banking product. The decision is made based on scoring. Scoring consists in assigning points for completing a certain questionnaire developed by underwriters of credit risk assessors. Based on the results of the points gained, the system automatically makes a decision on approving or refusing to issue a loan. Different banks have diffe¬rent scoring models. Purpose of the study. The paper considers the use of fuzzy models for making a decision by a bank to issue a banking product that implements the concept of “soft computing”. Methods. The use of fuzzy logic methods in credit scoring is not new, but it is not widely used in practice because it is expensive to integrate into existing systems. Each bank uses its own indicators of the client's financial reliability in scoring. Most of the indicators in banks are the same, but when deciding to issue different banking products, they have different numerical values. The data of the standard scoring methodology of a real bank were taken as the initial data. To predict a bank's decision to issue a banking product to a client, a fuzzy model was applied, production rules were proposed, and membership functions were determined. The model focused on the simultaneous processing of incoming data from multiple clients and for different banks and different scoring models. Results. The developed mathematical model for assessing the client's rating and predicting the decision to receive a banking product based on the fuzzy inference rule. The obtained results are proposed to be used in a multi-banking web-oriented system of providing banking products to corporate clients.\",\"PeriodicalId\":338904,\"journal\":{\"name\":\"Bulletin of the South Ural State University. Ser. Computer Technologies, Automatic Control & Radioelectronics\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Bulletin of the South Ural State University. Ser. Computer Technologies, Automatic Control & Radioelectronics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.14529/CTCR210207\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bulletin of the South Ural State University. Ser. Computer Technologies, Automatic Control & Radioelectronics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14529/CTCR210207","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Application of Fuzzy Logic to Assess Banks' Credit Risk
The banking system is a constantly evolving system. The information environment of the bank is growing, the volumes of processed information are increasing due to the growth of users and banking products. To reduce risks, banks make a financial assessment of the situation of individuals and legal entities. The aim of the work is to develop fuzzy multi-connected models designed to predict the receipt of a positive or negative decision to receive a banking product. The decision is made based on scoring. Scoring consists in assigning points for completing a certain questionnaire developed by underwriters of credit risk assessors. Based on the results of the points gained, the system automatically makes a decision on approving or refusing to issue a loan. Different banks have diffe¬rent scoring models. Purpose of the study. The paper considers the use of fuzzy models for making a decision by a bank to issue a banking product that implements the concept of “soft computing”. Methods. The use of fuzzy logic methods in credit scoring is not new, but it is not widely used in practice because it is expensive to integrate into existing systems. Each bank uses its own indicators of the client's financial reliability in scoring. Most of the indicators in banks are the same, but when deciding to issue different banking products, they have different numerical values. The data of the standard scoring methodology of a real bank were taken as the initial data. To predict a bank's decision to issue a banking product to a client, a fuzzy model was applied, production rules were proposed, and membership functions were determined. The model focused on the simultaneous processing of incoming data from multiple clients and for different banks and different scoring models. Results. The developed mathematical model for assessing the client's rating and predicting the decision to receive a banking product based on the fuzzy inference rule. The obtained results are proposed to be used in a multi-banking web-oriented system of providing banking products to corporate clients.