Hybrid Multi-Level Credit Card Fraud Detection System by Bagging Multiple Boosted Trees (BMBT)

Kavitha, M.; Suriakala, M.

doi:10.1109/iccic.2017.8524161

Cited by 6 publications

(1 citation statement)

References 19 publications

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“…Finally, Section 6 concludes the paper and provides some new directions to continue this research. [18], [19], [1], [20], [21], [22], [23], [24], [25] Bayesian Networks (BN) [26], [27], [28], [15], [15], [29], [30], [31], [32] Decision Trees (DT) [33], [19], [28], [15], [34], [35], [20], [36] [37] [38] [39]; [40], [41], [42], [43], [44], [25] Artificial Immune Systems (AIS) [45], [28], [15], [1], [46], [47] Naïve Bayes (NB) [48], [49], [50], [18], [19], [27], [15], [28], [20], [51], [52],…”

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Credit card fraud detection using asexual reproduction optimization

Ghahfarokhi

Mansouri

Moghaddam

et al. 2021

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PurposeThe 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/approachIn 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.FindingsResults 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/valueIn 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.

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