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

Abdeslam El,Sara HALOUI

doi:10.14569/ijacsa.2020.0110259

Abstract

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.

Highlights

In the banking environment, among a variety of risks to which a bank may be exposed, credit risk remains the biggest and most dangerous, its control and evaluation are essential steps to continually improve the performance of banks in the financial market [1]
3) Descriptive statistics: First, we present the descriptive statistics relating to the explanatory variables in Table III as follow: From the statistics above associated with the explanatory variables, we can observe a strong dispersion of the observations
1) The selection of the optimal model: we proceed to the tests of the choice of the most significant explanatory variables in relation to the variable to be explained, and we will do this through the method of elimination of nonsignificant variables at the threshold of 5% one by one in order to make a successive correction of the proposed model

Summary

INTRODUCTION

In the banking environment, among a variety of risks to which a bank may be exposed, credit risk remains the biggest and most dangerous, its control and evaluation are essential steps to continually improve the performance of banks in the financial market [1]. This necessarily involves the implementation of instruments and devices to anticipate and predict this type of risk. This paper will use a statistical model to predict the risk of bank credit

Credit Risk : A General View

Credit Risk and Basel Agreements

Statement of Outstanding Debts of Banks in Morocco

MODELING OF CREDIT RISK

Empirical Results

Findings

CONCLUSION