Tuning Machine Learning Models Using a Group Search Firefly Algorithm for Credit Card Fraud Detection

Dijana Jovanovic, Milos Antonijevic, Milos Stankovic, Miodrag Zivkovic, Marko Tanaskovic and Nebojsa Bacanin
Additional contact information
Dijana Jovanovic: College of Academic Studies Dositej, 11000 Belgrade, Serbia
Milos Antonijevic: Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11010 Belgrade, Serbia
Milos Stankovic: Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11010 Belgrade, Serbia
Miodrag Zivkovic: Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11010 Belgrade, Serbia
Marko Tanaskovic: Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11010 Belgrade, Serbia
Nebojsa Bacanin: Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11010 Belgrade, Serbia

Mathematics, 2022, vol. 10, issue 13, 1-30

Abstract: Recent advances in online payment technologies combined with the impact of the COVID-19 global pandemic has led to a significant escalation in the number of online transactions and credit card payments being executed every day. Naturally, there has also been an escalation in credit card frauds, which is having a significant impact on the banking institutions, corporations that issue credit cards, and finally, the vendors and merchants. Consequently, there is an urgent need to implement and establish proper mechanisms that can secure the integrity of online card transactions. The research presented in this paper proposes a hybrid machine learning and swarm metaheuristic approach to address the challenge of credit card fraud detection. The novel, enhanced firefly algorithm, named group search firefly algorithm, was devised and then used to a tune support vector machine, an extreme learning machine, and extreme gradient-boosting machine learning models. Boosted models were tested on the real-world credit card fraud detection dataset, gathered from the transactions of the European credit card users. The original dataset is highly imbalanced; to further analyze the performance of tuned machine learning models, in the second experiment performed for the purpose of this research, the dataset has been expanded by utilizing the synthetic minority over-sampling approach. The performance of the proposed group search firefly metaheuristic was compared with other recent state-of-the-art approaches. Standard machine learning performance indicators have been used for the evaluation, such as the accuracy of the classifier, recall, precision, and area under the curve. The experimental findings clearly demonstrate that the models tuned by the proposed algorithm obtained superior results in comparison to other models hybridized with competitor metaheuristics.

Keywords: machine learning; credit card fraud; metaheuristic algorithms; swarm intelligence; artificial intelligence; firefly algorithm; optimization; classification (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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