Open AccessComparison of Different Ensemble Methods in Credit Card Default Prediction
Author(s) -
Azhi Abdalmohammed Faraj,
Didam Ahmed Mahmud,
Bilal Rashid
Publication year - 2021
Publication title -
uhd journal of science and technology
Language(s) - English
Resource type - Journals
eISSN - 2521-4217
pISSN - 2521-4209
DOI - 10.21928/uhdjst.v5n2y2021.pp20-25
Subject(s) - default , computer science , machine learning , credit card , artificial intelligence , ensemble learning , credit card fraud , payment , artificial neural network , receiver operating characteristic , finance , world wide web , economics
Credit card defaults pause a business-critical threat in banking systems thus prompt detection of defaulters is a crucial and challenging research problem. Machine learning algorithms must deal with a heavily skewed dataset since the ratio of defaulters to non-defaulters is very small. The purpose of this research is to apply different ensemble methods and compare their performance in detecting the probability of defaults customer’s credit card default payments in Taiwan from the UCI Machine learning repository. This is done on both the original skewed dataset and then on balanced dataset several studies have showed the superiority of neural networks as compared to traditional machine learning algorithms, the results of our study show that ensemble methods consistently outperform Neural Networks and other machine learning algorithms in terms of F1 score and area under receiver operating characteristic curve regardless of balancing the dataset or ignoring the imbalance