Abstract: The credit scoring aim is to classify the customer credit as defaulter or non-defaulter. The credit risk analysis is more effective with further boosting and smoothing of the parameters of models. The objective of this paper is to explore the credit score classification models with an imputation technique and without imputation technique. However, data availability is low in case of without imputation because of missing values depletion from the large dataset. On the other hand, imputation based dataset classification accuracy with linear method of ANN is better than other models. The comparison of models with boosting and smoothing shows that error rate is better metric than area under curve (AUC) ratio. It is concluded that artificial neural network (ANN) is better alternative than decision tree and logistic regression when data availability is high in dataset.

Sharjeel Imtiaz and Allan J. Brimicombe, “A Better Comparison Summary of Credit Scoring Classification” International Journal of Advanced Computer Science and Applications(IJACSA), 8(7), 2017. http://dx.doi.org/10.14569/IJACSA.2017.080701

@article{Imtiaz2017,
title = {A Better Comparison Summary of Credit Scoring Classification},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.080701},
url = {http://dx.doi.org/10.14569/IJACSA.2017.080701},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {7},
author = {Sharjeel Imtiaz and Allan J. Brimicombe}
}