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 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.

@article{HALOUI2020,
  title     = {An Optimal Prediction Model’s Credit Risk: The Implementation of the Backward Elimination and Forward Regression Method},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {2},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Sara HALOUI and Abdeslam El MOUDDEN},
  doi       = {10.14569/IJACSA.2020.0110259},
  url       = {https://doi.org/10.14569/IJACSA.2020.0110259}
}