Credit Card Fraud Detection Using a New Hybrid Machine Learning Architecture

Esraa Faisal Malik, Khai Wah Khaw, Bahari Belaton, Wai Peng Wong and XinYing Chew
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Esraa Faisal Malik: School of Management, Universiti Sains Malaysia, Penang 11800, Malaysia
Khai Wah Khaw: School of Management, Universiti Sains Malaysia, Penang 11800, Malaysia
Bahari Belaton: School of Computer Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia
Wai Peng Wong: School of Information Technology, Monash University, Malaysia Campus, Subang Jaya 47500, Malaysia
XinYing Chew: School of Computer Sciences, Universiti Sains Malaysia, Penang 11800, Malaysia

Mathematics, 2022, vol. 10, issue 9, 1-16

Abstract: The negative effect of financial crimes on financial institutions has grown dramatically over the years. To detect crimes such as credit card fraud, several single and hybrid machine learning approaches have been used. However, these approaches have significant limitations as no further investigation on different hybrid algorithms for a given dataset were studied. This research proposes and investigates seven hybrid machine learning models to detect fraudulent activities with a real word dataset. The developed hybrid models consisted of two phases, state-of-the-art machine learning algorithms were used first to detect credit card fraud, then, hybrid methods were constructed based on the best single algorithm from the first phase. Our findings indicated that the hybrid model Adaboost + LGBM is the champion model as it displayed the highest performance. Future studies should focus on studying different types of hybridization and algorithms in the credit card domain.

Keywords: classification; credit card; data mining; fraud detection; hybrid; machine learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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