TY  - JOUR
T1  - An Application of Artificial Intelligent Neural
Network and Discriminant Analyses on Credit Scoring
AU - Alabi, M.A. AU - Issa, S. AU - Afolayan, R.B. 
JO  - Journal of Modern Mathematics and Statistics
VL  - 7
IS  - 4
SP  - 47
EP  - 54
PY  - 2013
DA  - 2001/08/19
SN  - 1994-5388
DO  - jmmstat.2013.47.54
UR  - https://makhillpublications.co/view-article.php?doi=jmmstat.2013.47.54
KW  - Credit scoring
KW  -artificial neural network
KW  -discriminant analysis
KW  -linear discrimination model
KW  -optimization algorithm
AB  - The research paper deals with credit scoring in banking system which compares most commonly
statistical predictive model for credit scoring, Artificial intelligent Neural Network (ANN) and discriminant
analyses. It is very clear from the classification outcomes of this research that neural network compares well
with linear discriminant model. It gives slight better results than discriminant analysis. However, it is
noteworthy that a bad accepted generates much high costs than a good rejected and neural network acquires
less amount of bad accepted than the discriminant predictive model. It achieves less cost of misclassification
for the data set use in the research. Furthermore, if the final section of this research, an optimization algorithm
(genetic algorithm) is proposed in order to obtain better classification accuracy through the configuration of
the neural network architecture. On the contrary, it is important to note that the success of the predictive model
largely depends on the predictor variables selection to be used as inputs of the model.
ER  -