Credit card fraud detection using asexual reproduction optimization

Farhang, A; Mansouri, T; Sadeghi Moghaddam, MR; Bahrambeik, N; Yavari, R; Fani Sani, MR

Abstract

Purpose – The best algorithm that was implemented on this Brazilian dataset was artificial immune system (AIS)
algorithm. But the time and cost of this algorithm are high. Using asexual reproduction optimization (ARO) algorithm,
the authors achieved better results in less time. So the authors achieved less cost in a shorter time. Their framework
addressed the problems such as high costs and training time in credit card fraud detection. This simple and effective
approach has achieved better results than the best techniques implemented on our dataset so far. The purpose of this
paper is to detect credit card fraud using ARO.
Design/methodology/approach – In this paper, the authors used ARO algorithm to classify the bank transactions
into fraud and legitimate. ARO is taken from asexual reproduction. Asexual reproduction refers to a kind of production in
which one parent produces offspring identical to herself. In ARO algorithm, an individual is shown by a vector of variables.
Each variable is considered as a chromosome. A binary string represents a chromosome consisted of genes. It is supposed
that every generated answer exists in the environment, and because of limited resources, only the best solution can remain
alive. The algorithm starts with a random individual in the answer scope. This parent reproduces the offspring named bud.
Either the parent or the offspring can survive. In this competition, the one which outperforms in fitness function remains
alive. If the offspring has suitable performance,it will be the next parent, and the current parent becomes obsolete.Otherwise,
the offspring perishes, and the present parent survives. The algorithm recurs until the stop condition occurs.
Findings – Results showed that ARO had increased the AUC (i.e. area under a receiver operating characteristic (ROC)
curve), sensitivity, precision, specificity and accuracy by 13%, 25%, 56%, 3% and 3%, in comparison with AIS,
respectively. The authors achieved a high precision value indicating that if ARO detects a record as a fraud, with a high
probability, it is a fraud one. Supporting a real-time fraud detection system is another vital issue. ARO outperforms AIS
not only in the mentioned criteria, but also decreases the training time by 75% in comparison with the AIS, which is a
significant figure.
Originality/value – In this paper, the authors implemented the ARO in credit card fraud detection. The authors
compared the results with those of the AIS, which was one of the best methods ever implemented on the benchmark
dataset. The chief focus of the fraud detection studies is finding the algorithms that can detect legal transactions from
the fraudulent ones with high detection accuracy in the shortest time and at a low cost. That ARO meets all these
demands.

Citation

Farhang, A., Mansouri, T., Sadeghi Moghaddam, M., Bahrambeik, N., Yavari, R., & Fani Sani, M. (2021). Credit card fraud detection using asexual reproduction optimization. Kybernetes, https://doi.org/10.1108/K-04-2021-0324

• Journal Article Type: Article
• Acceptance Date: Jun 28, 2021
• Online Publication Date: Jul 27, 2021
• Publication Date: Jul 27, 2021
• Deposit Date: Jun 29, 2021
• Publicly Available Date: Aug 2, 2021
• Journal: Kybernetes
• Print ISSN: 0368-492X
• Publisher: Emerald
• DOI: https://doi.org/10.1108/K-04-2021-0324
• Publisher URL: https://doi.org/10.1108/K-04-2021-0324
• Related Public URLs: http://www.emeraldinsight.com/loi/k