An Optimal Prediction Model’s Credit Risk: The Implementation of the Backward Elimination and Forward Regression Method

The purpose of this paper is to verify whether there is a relationship between credit risk, main threat to the banks, and the demographic, marital, cultural and socio-economic characteristics of a sample of 40 credit applicants, by using the optimal backward elimination model and the forward regress...

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Bibliographic Details
Published in:International journal of advanced computer science & applications 2020, Vol.11 (2)
Main Authors: HALOUI, Sara, El, Abdeslam
Format: Article
Language:English
Subjects:
Bank failures
Banking industry
Capital requirements
Computer science
Credit policy
Credit risk
Debt
Insolvency
International banking
Laboratories
Prediction models
Regression models
Regulation of financial institutions
Risk
Securities markets
Statistical analysis
Statistical models
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Summary:The purpose of this paper is to verify whether there is a relationship between credit risk, main threat to the banks, and the demographic, marital, cultural and socio-economic characteristics of a sample of 40 credit applicants, by using the optimal backward elimination model and the forward regression method. Following the statistical modeling, the final result allows us to know the variables that have a degree of significance lower than 5%, and therefore a significant relationship with the credit risk, namely the CSP (Socio-occupational category), the amount of credit requested, the repayment term and the type of credit. However, by implementing the second method, the place of residence variable was selected as an impacting variable for the chosen model. Overall, these features will help us better predict the risk of bank credit.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2020.0110259