Credit Scoring Refinement using Optimized Logistic Regression

A poor credit scoring model will give a poor power
for predicting defaulted loan. There are many approaches for
modeling the default prediction, such as classical logistic
regression and Bayesian logistics regression. In this paper, we
applied both classical logistic regression and AUC (Area under
Curved) optimized using Nelder-Mead Algorithm for refining a
credit scoring model that has already been used for several years
by an International bank in Indonesia. Both classical logistics
regression and AUC optimized method perform well in improving
the model, but logistic regression still better in some aspects. AUC
Optimized model has higher AUC than logistic regression model
but has lower Kolmogorov-Smirnov Score (KS-Score)