Fig. 5
From: A dynamic credit risk assessment model with data mining techniques: evidence from Iranian banks
The performance with the dynamic modeling- full and short memory- versus the static model. This Fig shows the performance of dynamic modeling – full and short versus the static model. A dynamical modeling framework for credit risk assessment was recently proposed by Maria Rocha Sousa et al. (Sousa & Gama, 2016) have proven to outperform static models in helping the banks to prevent the probable future losses, has some shortcomings. The vertical axis shows the average of NPL and the horizontal axis shows the year. The charts show the NPL at each year by short memory way, full memory way and static model