Credit Card Fraud Detection Using AdaBoost and Majority Voting
By Kuldeep Randhawa, Chu Kiong Loo, Manjeevan Seera, Chee Peng Lim, Asoke Kumar Nandi. IEEE Access, 2019
405 citations recorded by the source corpus.
Credit card fraud is a serious problem in financial services. Billions of dollars are lost due to credit card fraud every year. There is a lack of research studies on analyzing real-world credit card data owing to confidentiality issues. In this paper, machine learning algorithms are used to detect credit card fraud. Standard models are first used. Then, hybrid methods which use AdaBoost and majority voting methods are applied. To evaluate the model efficacy, a publicly available credit card data set is used. Then, a real-world credit card data set from a financial institution is analyzed. In addition, noise is added to the data samples to further assess the robustness of the algorithms. The experimental results positively indicate that the majority voting method achieves good accuracy rates in detecting fraud cases in credit cards.
Subfields: Accounting, Artificial Intelligence, Political Science and International Relations
View Paper: https://www.semanticscholar.org/paper/aff9897e0acf74aca20acf3d76c621dee9fd169c
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Citations in the corpus
- An Intelligent Approach to Credit Card Fraud Detection Using an Optimized Light Gradient Boosting Machine (2020, background): “It is well known that the performance of various machine learning methods decreases when the analyzed data set is unbalanced [32].”
- Ensemble deep learning: A review (2021, methodology): “2018 Novel genetic-based negative correlation learning for estimating soil temperature [216] Negative correlation learning Soil Temperature Estimation 2018 Credit Card Fraud Detection Using Ad- aBoost and Majority Voting [217] AdaBoost Credit Card Fraud Detection 2018 Sparse Deep Stacking Network fo”