Abstract
Credit risk evaluation is getting importance due to competition and also the
Basel requirements. This study is based on the evaluation of credit scoring
models for retail bank loan customers of Askari Bank Limited in Pakistan.
Credit risk models further brings efficiency in the evaluation of a loan
application which ultimately reduces the overall risk of bank and also reduces
the probability of default on loans. This study has used three credit scoring
models of logistic regression, discriminant analysis and probit analysis for the
evaluation of credit applicants. The ranking criteria for ranking among the
various models is based on the average correct classification rate. Therefore,
the ranking shows that probit analysis techniques has the highest average
correct classification rate while Logistic regression technique has the lowest
average correct classification rate. These statistical credit scoring approach
further augments the judgmental approach of credit application evaluation
procedure. Further studies can be carried out on an extended data sample of
retail loans. Other important variables can be included in the model to further
improve the validity of the credit evaluation procedure according to the
regulatory requirements. The statistical procedures of decision trees and neural
networks can also be applied in credit scoring procedures to further make it
more authentic and reliable.
Keywords: credit scoring, retail banking, discriminant analysis, probit
analysis, logistic regression
Arif Hussain, muhammad khan. (2019) Credit Scoring Model for Retail Banking Sector in Pakistan, Journal of Managerial Sciences, Volume 13, Issue 4.
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