scholarly journals Default Predictors in Retail Credit Scoring: Evidence from Czech Banking Data

2011 ◽  
Author(s):  
Evzen Kocenda ◽  
Martin Vojtek
Keyword(s):  
Credit Scoring ◽  
Retail Credit

scholarly journals Variable reduction, sample selection bias and bank retail credit scoring

2010 ◽  
Vol 17 (3) ◽  
pp. 501-512 ◽  
Author(s):  
Andrew Marshall ◽  
Leilei Tang ◽  
Alistair Milne
Keyword(s):  
Selection Bias ◽  
Credit Scoring ◽  
Sample Selection ◽  
Sample Selection Bias ◽  
Retail Credit ◽  
Variable Reduction

scholarly journals Default Predictors in Retail Credit Scoring: Evidence from Czech Banking Data

2011 ◽  
Vol 47 (6) ◽  
pp. 80-98 ◽  
Author(s):  
Evžen Kočenda ◽  
Martin Vojtek
Keyword(s):  
Credit Scoring ◽  
Retail Credit

scholarly journals Retail credit scoring using fine‐grained payment data

2019 ◽  
Vol 182 (4) ◽  
pp. 1227-1246 ◽  
Author(s):  
Ellen Tobback ◽  
David Martens
Keyword(s):  
Credit Scoring ◽  
Fine Grained ◽  
Retail Credit

Retail Credit Risk Management in Indian Public Sector Banks

2012 ◽  
Vol 3 (8) ◽  
pp. 31-37
Author(s):  
Nayan J. Nayan J. ◽  
◽  
Dr. M. Kumaraswamy Dr. M. Kumaraswamy
Keyword(s):  
Risk Management ◽  
Public Sector ◽  
Credit Risk ◽  
Credit Risk Management ◽  
Public Sector Banks ◽  
Retail Credit

TOOLS AND PECULIARITIES OF SOLVING THE CLASSIFICATION PROBLEM IN CREDIT SCORING SYSTEMS

2020 ◽  
Vol 17 (1) ◽  
pp. 51-59
Author(s):  
A.A. Grishin ◽  
◽  
S.P. Stroyev ◽  
Keyword(s):  
Credit Scoring ◽  
Scoring Systems ◽  
Classification Problem

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.

Credit scoring optimization using the area under the curve

2014 ◽  
Vol 8 (1) ◽  
pp. 31-67 ◽  
Author(s):  
Anne Kraus ◽  
Helmut Küchenhoff
Keyword(s):  
Credit Scoring ◽  
Area Under The Curve
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