scholarly journals Credit Scoring and Default Risk Prediction: A Comparative Study between Discriminant Analysis & Logistic Regression

2016 ◽  
Vol 8 (4) ◽  
pp. 39 ◽  
Author(s):  
Zaghdoudi Khemais ◽  
Djebali Nesrine ◽  
Mezni Mohamed
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Comparative Study ◽  
Risk Prediction ◽  
Commercial Bank ◽  
Default Risk ◽  
Small And Medium Enterprises ◽  
Credit Scoring ◽  
Linear Discrimination ◽  
Medium Enterprises

<p>This paper aims to develop models for foreseeing default risk of small and medium enterprises (SMEs) for one Tunisian commercial bank using two different methodologies (logistic regression and discriminant analysis). We used a database that consists of 195 credit files granted to Tunisian SMEs which are divided into five sectors “industry, agriculture, tourism, trade and services” for a period from 2012 to 2014. The empirical results that we found support the idea that these two scoring techniques have a statistically significant power in predicting default risk of enterprises. Logistic discrimination classifies enterprises correctly in their original groups with a rate of 76.7% against 76.4% in case of linear discrimination giving so a slight superiority to the first method.</p>

Regression model of assessment of customer solvency and banking risks in the process of lending

2019 ◽  
pp. 69-73
Author(s):  
Zoryna Yurynets ◽  
Rostyslav Yurynets ◽  
Nataliya Kunanets ◽  
Ivanna Myshchyshyn
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Regression Model ◽  
Likelihood Ratio ◽  
Credit Scoring ◽  
Bank Loans ◽  
Management Decisions ◽  
Computer Data ◽  
Proposed Model ◽  
Loan Risk

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.

scholarly journals Determining factors in MSMEs success: An empirical study in Mexico

2022 ◽  
Vol 6 (1) ◽  
pp. e384
Author(s):  
Rubén Molina-Sánchez ◽  
Domingo García-Pérez-de-Lema ◽  
Alejandra López-Salazar ◽  
Roberto Godínez-López
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Empirical Study ◽  
Small And Medium Enterprises ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Analysis ◽  
Technology And Innovation ◽  
Determining Factors ◽  
Quality Technology ◽  
Medium Enterprises

This work empirically analyzes the competitive factors that help make micro, small, and medium enterprises (MSMEs) successful. To do this, an empirical study with a sample of 614 companies in Guanajuato, Mexico, has been carried out. The results of the binary logistic regression analysis show that quality, technology, and innovation are the main variables that determine a company’s success. These findings could provide guidelines to help MSMEs improve their competitiveness, and they could help public administrations better support MSME growth.

scholarly journals Motives Behind The Integration Of CSR Into Business Strategy: A Comparative Study In French SMEs

2015 ◽  
Vol 31 (5) ◽  
pp. 1975 ◽  
Author(s):  
Linh-Chi Vo ◽  
Karen Delchet-Cochet ◽  
Hakim Akeb
Keyword(s):  
Corporate Social Responsibility ◽  
Social Responsibility ◽  
Comparative Study ◽  
Business Strategy ◽  
Small And Medium Enterprises ◽  
Operational Performance ◽  
Integrative Study ◽  
Medium Enterprises ◽  
Corporate Social ◽  
Shed Light

<p>Corporate social responsibility (CSR) in the context of small and medium enterprises (SMEs) has become an important and substantial area of study for quite a few years. In this literature, while so much research has shed light on what makes SMEs integrate CSR into their business strategy, the existing results regarding their economic, social, and environmental motives are contradictory. In this article, we aim at making a contribution by conducting an integrative study. More specifically, we compare the roles of economic, social, and environmental motives in driving SMEs to make CSR become an integral part of their strategic planning and routine operational performance. Our sample includes 155 French SMEs.</p>

scholarly journals Credit Scoring in SME Asset-Backed Securities: An Italian Case Study

2019 ◽  
Vol 12 (2) ◽  
pp. 89 ◽  
Author(s):  
Andrea Bedin ◽  
Monica Billio ◽  
Michele Costola ◽  
Loriana Pelizzon
Keyword(s):  
Small And Medium Enterprises ◽  
Credit Scoring ◽  
Bank Lending ◽  
Default Probability ◽  
Loss Distribution ◽  
Recovery Rates ◽  
Italian Case ◽  
Level Data ◽  
Asset Backed Securities ◽  
Medium Enterprises

We investigate the default probability, recovery rates and loss distribution of a portfolio of securitised loans granted to Italian small and medium enterprises (SMEs). To this end, we use loan level data information provided by the European DataWarehouse platform and employ a logistic regression to estimate the company default probability. We include loan-level default probabilities and recovery rates to estimate the loss distribution of the underlying assets. We find that bank securitised loans are less risky, compared to the average bank lending to small and medium enterprises.

Hybrid Model for Credit Risk Prediction: An Application of Neural Network Approaches

2019 ◽  
Vol 28 (05) ◽  
pp. 1950017 ◽  
Author(s):  
Guotai Chi ◽  
Mohammad Shamsu Uddin ◽  
Mohammad Zoynul Abedin ◽  
Kunpeng Yuan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Logistic Regression ◽  
Credit Risk ◽  
Risk Prediction ◽  
Hybrid Model ◽  
Credit Scoring ◽  
Hybrid Models ◽  
Multilayer Perceptrons ◽  
Inference Systems

Credit risk prediction is essential for banks and financial institutions as it helps them to evade any inappropriate assessments that can lead to wasted opportunities or monetary losses. In recent times, the hybrid prediction model, a combination of traditional and modern artificial intelligence (AI) methods that provides better prediction capacity than the use of single techniques, has been introduced. Similarly, using conventional and topical artificial intelligence (AI) technologies, researchers have recommended hybrid models which amalgamate logistic regression (LR) with multilayer perceptron (MLP). To investigate the efficiency and viability of the proposed hybrid models, we compared 16 hybrid models created by combining logistic regression (LR), discriminant analysis (DA), and decision trees (DT) with four types of neural network (NN): adaptive neurofuzzy inference systems (ANFISs), deep neural networks (DNNs), radial basis function networks (RBFs) and multilayer perceptrons (MLPs). The experimental outcome, investigation, and statistical examination express the capacity of the planned hybrid model to develop a credit risk prediction technique different from all other approaches, as indicated by ten different performance measures. The classifier was authenticated on five real-world credit scoring data sets.

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