Assessing individual credit risk on the basis of discriminant analysis by Poland's cooperative banks

2017 ◽  
Vol 7 (2) ◽  
pp. 103
Author(s):  
Rafał Balina ◽  
Mirosława Nowak
Keyword(s):  
Discriminant Analysis ◽  
Credit Risk ◽  
Cooperative Banks

scholarly journals Drivers of Individual Credit Risk of Retail Customers—A Case Study on the Example of the Polish Cooperative Banking Sector

Risks ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 219
Author(s):  
Rafał Balina ◽  
Marta Idasz-Balina
Keyword(s):  
Credit Risk ◽  
Financial Institutions ◽  
Predictive Power ◽  
Banking Sector ◽  
Key Factors ◽  
Logit Regression ◽  
Local Character ◽  
History Of ◽  
Cooperative Banks

The main aim of the research was to determine the key factors determining the level of credit risk of individual clients (clients in the form of natural persons, excluding companies) on the example of Polish cooperative banks according to the following features: transaction characteristics, socio-demographic characteristics of the customer, the customer’s financial situation, the customer’s history of cooperation with the cooperative bank where they applied for a loan, and the customer’s history of cooperation with other financial institutions. For the research gathered data from 1000 credit applications submitted by individual customers when applying for a credit in five different cooperative banks were used for the analyses. To assess the credit risk of retail clients we use logit regression models, and additionally, score cards were calculated. The results of the research indicate that among the factors with high predictive power there were the features characterizing the client’s history of cooperation with the cooperative bank, where they applied for a loan. It may mean that when assessing credit risk related to financing individual customers, cooperative banks due to their local character, have an advantage over other financial institutions.

scholarly journals Determinants of Loan Quality: Lessons from Greek Cooperative Banks

2016 ◽  
Vol 9 (1) ◽  
pp. 115-140
Author(s):  
Vasiliki Makri ◽  
Konstantinos Papadatos
Keyword(s):  
Credit Risk ◽  
Public Debt ◽  
Economic Activity ◽  
Loan Loss ◽  
Explanatory Variables ◽  
Regression Techniques ◽  
Cooperative Banks ◽  
Loan Quality ◽  
Accounting Ratios ◽  
Macroeconomic Environment

AbstractThe article focuses on the credit risk of cooperative banks in Greece. The main objective is to define which factors are responsible for variations in loan quality during the period 2003-2014. Loan quality is measured by Loan Loss Reserves Ratio (LLR) and dynamic regression techniques are implemented for the econometric estimations. The outlined results suggest that the macroeconomic environment (i.e. public debt, local unemployment, economic activity and inflation) and the accounting ratios (i.e. past loan quality and profitability) seem to be the explanatory variables of problem loans.

Discriminant Analysis and Logistic Regression Applying To Credit Risk Management

2021 ◽  
Vol 6 (3) ◽  
Author(s):  
Ngongo Isidore ◽  
◽  
Etoua Magloire ◽  
Jimbo Claver ◽  
Mengue Mvondo Jenner ◽  
...  
Keyword(s):  
Risk Management ◽  
Logistic Regression ◽  
Discriminant Analysis ◽  
Financial Crisis ◽  
Credit Risk ◽  
Efficient Method ◽  
Credit Risk Management ◽  
Fisher Discriminant Analysis ◽  
Fisher Discriminant ◽  
The World

The financial crisis that is currently shaking the world, particularly the successive failures of the major banks have brought the issue of banking risks, including credit risk, back to the forefront. This risk must now be managed by more sophisticated methods. In this paper we present two methods that allow us to establish two functions, namely Fisher discriminant analysis and logistic regression; these two functions allow us to evaluate the risk of non-repayment incurred by a bank in the light of our data. It emerges that Fisher discriminant analysis is more effective or efficient than logistic regression for the evaluation of the risk of non-repayment of credit. Discriminant analysis and logistic regression are two methods of credit risk management here the problem we are trying to solve is how to help banks choose the most efficient method between the latter two.

scholarly journals Die voorspelling van kredietrisiko met behulp van biografiese veranderlikes: Is dit die antwoord?

1983 ◽  
Vol 14 (4) ◽  
pp. 166-171
Author(s):  
P. L.S. Ackermann ◽  
W. P. Jansen van Rensburg
Keyword(s):  
Regression Analysis ◽  
Discriminant Analysis ◽  
Credit Risk ◽  
Representative Sample ◽  
Stepwise Regression ◽  
Stepwise Regression Analysis ◽  
Stepwise Discriminant Analysis ◽  
Relative Importance ◽  
Multiple Stepwise Regression Analysis

The prediction of credit risk by means of biographic variables: Is this the answer? The objective of this study is to identify specific biographical variables, to quantity them and to investigate their relative importance in the prediction of credit risk. A representative sample of 250 bad credit risk clients and 250 good credit risk clients is used in the study. A multiple stepwise regression analysis and multiple stepwise discriminant analysis were carried out. Nine biographical variables were identified which explain approximately 16% of the variance of credit risk.

scholarly journals Credit risk assessment of agricultural enterprises in the Republic of Serbia: Logistic regression vs discriminant analysis

2021 ◽  
Vol 68 (4) ◽  
pp. 881-894
Author(s):  
Dragana Tekić ◽  
Beba Mutavdžić ◽  
Dragan Milić ◽  
Nebojša Novković ◽  
Vladislav Zekić ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Logistic Regression ◽  
Discriminant Analysis ◽  
Credit Risk ◽  
Regression Model ◽  
Financial Stability ◽  
Logistic Regression Model ◽  
Agricultural Enterprises ◽  
Credit Risk Assessment ◽  
The Republic

Credit risk assessment of agricultural enterprises in the Republic of Serbia was analyzed in this research by applying discriminant analysis and logistic regressions. The aim of the research is to determine the financial indicators which financial analysts consider when analyzing a loan application that have the most influence on the decision to approve or reject a loan application. The internal determinants of credit risk of agricultural enterprises are analyzed, i.e., indicators of financial leverage, profitability, liquidity, solvency, financial stability and effectiveness. The analyzed models gave different results in significance of the observed indicators. The indicators that stood out as significant in both models are only indicators of profitability and solvency. The model of discriminant analysis has successfully classified rate 81.0%, while the logistic regression model has successfully classifies rate 89.8%. In modeling the credit risk of agricultural enterprises in the Republic of Serbia, the logistic regression model gives better results.

scholarly journals Design and Development of Credit Scoring Model for Conventional Banks for Individual Borrowing Case Study on PT BPR Sungai Puar District Agam

2018 ◽  
Vol 1 (1) ◽  
pp. 43-56
Author(s):  
Rio Hendriadi ◽  
Anne Putri ◽  
Dona Amelia ◽  
Rany Syafrina
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Credit Risk ◽  
Credit Scoring ◽  
Scoring Model ◽  
Secondary Sources ◽  
Conventional Banks ◽  
Credit Scoring Model ◽  
Conventional Bank

Objective – This research is conducted to design and to develop credit scoring model on conventional bank in order to determine individual loan, the research takes place in PT BPR Sungai Puar, Kabupaten Agam. This model tries to evaluate the credit risk of BPR Sungai Puar.Design/methodology – The data are considered as secondary sources as they are taken from BPR Sungai Puar database by classifying them into two analysis tools including discriminant analysis and logistic regression. Results – The resuts are presentes inform of model and credit scoring perfection on PT BPR Sungai Puar Kabupaten Agam.Keywords Credit Scoring Model, Conventional Banks, Individual Loan

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