Creditor Classification Logistic Regression Ensemble Boosting And Logistic Regression In Creditor Classification With Binary Response

Abstract

Credit risk is the risk that has the greatest opportunity to occur in banking. The number of bad loans will also affect bank performance. The banking sector needs to know whether a prospective creditor is classified as a risky person or not. The purpose of this study is to classify creditors and compare the classification results through logistic regression with the maximum likelihood model and the Boosting algorithm, especially the AdaBoost algorithm, and to select a model with the Boosting algorithm Credit Scoring aims to classify prospective creditor into two classes, namely good prospective creditor (Performing Loan) and bad prospective creditor (Non Performing Loan) based on certain characteristics. The method often used for classifying creditor is logistic regression, but this method is less robust and less accurate than data mining. Thus, there is a need for methods that provide greater accuracy. Among the methods that have been proposed is a method called Boosting, which operates sequentially by applying a classification algorithm to the reweighted version of the training data set. This study uses 5 datasets. The first dataset is secondary data originating from data on non-subsidized homeownership creditors of Bank X Malang City. While the other datasets are simulation data with many samples of 10, 500, and 1000. The results of this study indicate that ensemble boosting logistic regression is more suitable for describing binary response problems, especially creditor classification because it provides more accurate information. For high-dimensional data, which is represented by a sample size of 10, ensemble logistic regression is proven to be able to produce fairly accurate predictions with an accuracy rate of up to 80%, whereas in the logistic regression analysis the model raises N.A because many samples < many independent variables. The use of boosting is preferred because it focuses on problems that are misclassified and have a tendency to increase to higher accuracy.