Journal
MATHEMATICS
Volume 10, Issue 9, Pages -Publisher
MDPI
DOI: 10.3390/math10091480
Keywords
classification; credit card; data mining; fraud detection; hybrid; machine learning
Categories
Funding
- Universiti Sains Malaysia, Short Term Grant [304/PMGT/6315513]
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The negative effect of financial crimes on financial institutions has grown over the years. Researchers propose and investigate seven hybrid machine learning models to detect fraudulent activities, finding that Adaboost + LGBM is the best performing model.
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.
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