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- Title
Comparison of Different Ensemble Methods in Credit Card Default Prediction.
- Authors
Faraj, Azhi Abdalmohammed; Mahmud, Didam Ahmed; Rashid, Bilal Najmaddin
- Abstract
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.
- Subjects
CREDIT cards; DEFAULT (Finance); PREDICTION models; MACHINE learning; ARTIFICIAL neural networks
- Publication
UHD Journal of Science & Technology, 2021, Vol 5, Issue 2, p20
- ISSN
2521-4209
- Publication type
Article
- DOI
10.21928/uhdjst.v5n2y2021.pp20-25