scholarly journals UMA PROPOSIÇÃO DE MODELOS DE PREVISÃO DE RISCO DE CRÉDITO PARA PEQUENAS E MÉDIAS EMPRESAS POR MEIO DA REGRESSÃO LOGÍSTICA

2022 ◽  
Vol 38 (113) ◽  
Author(s):  
Flávio Führ ◽  
José Donizetti de Lima ◽  
Gilson Ditzel Santos ◽  
Sady Mazzioni
Keyword(s):  

RESUMO A busca por padrões que contribuam na predição de risco, é crescente nas organizações. A utilização de modelos de credit scoring busca auxiliar o analista de crédito na tomada de decisão. Este trabalho objetiva elaborar procedimentos metodológicos, para estruturar e melhorar os modelos de credit scoring direcionados a análise de pequenas e médias empresas. Com a utilização da técnica estatística da regressão logística, por meio das melhorias elaboradas nos procedimentos metodológicos, como exemplo: divisão da base de dados em classes conforme enquadramento das empresas, foi possível o desenvolvimento de 5 modelos de credit scoring, sendo um modelo para cada classe de empresas e outro para a base geral de dados. Os modelos foram direcionados às entidades de fomento e concessão de crédito para pequenas e médias empresas. As acurácias dos modelos apresentaram percentuais expressivos para base de dados com variáveis não contábeis e não auditáveis, atingindo percentuais satisfatórios.

Author(s):  
Zoryna Yurynets ◽  
Rostyslav Yurynets ◽  
Nataliya Kunanets ◽  
Ivanna Myshchyshyn

In the current conditions of economic development, it is important to pay attention to the study of the main types of risks, effective methods of evaluation, monitoring, analysis of banking risks. One of the main approaches to quantitatively assessing the creditworthiness of borrowers is credit scoring. The objective of credit scoring is to optimize management decisions regarding the possibility of providing bank loans. In the article, the scientific and methodological provisions concerning the formation of a regression model for assessing bank risks in the process of granting loans to borrowers has been proposed. The proposed model is based on the use of logistic regression tools, discriminant analysis with the use of expert evaluation. During the formation of a regression model, the relationship between risk factors and probable magnitude of loan risk has been established. In the course of calculations, the coefficient of the individual's solvency has been calculated. Direct computer data preparation, including the calculation of the indicators selected in the process of discriminant analysis, has been carried out in the Excel package environment, followed by their import into the STATISTICA package for analysis in the “Logistic regression” sub-module of the “Nonlinear evaluation” module. The adequacy of the constructed model has been determined using the Macfaden's likelihood ratio index. The calculated value of the Macfaden's likelihood ratio index indicates the adequacy of the constructed model. The ability to issue loans to new clients has been evaluated using a regression model. The conducted calculations show the possibility of granting a loan exclusively to the second and third clients. The offered method allows to conduct assessment of client's solvency and risk prevention at different stages of lending, facilitates the possibility to independently make informed decisions on credit servicing of clients and management of a loan portfolio, optimization of management decisions in banks. In order for a loan-based model to continue to perform its functions, it must be periodically adjusted.


2014 ◽  
Vol 8 (1) ◽  
pp. 31-67 ◽  
Author(s):  
Anne Kraus ◽  
Helmut Küchenhoff

2012 ◽  
Vol 5 (1) ◽  
pp. 11-20
Author(s):  
Javier de Andres ◽  
Pedro Lorca ◽  
Fernando Sanchez-Lasheras ◽  
Francisco Javier De Cos-Juez

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