Browsing by Author "Elikucuk, I."

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Applying migrating birds optimization to credit card fraud detection
(Springer Science+Business Media, 2013) Elikucuk, I.; Duman, Ekrem; Industrial Engineering; DUMAN, Ekrem
We discuss how the Migrating Birds Optimization algorithm (MBO) is applied to statistical credit card fraud detection problem. MBO is a recently proposed metaheuristic algorithm which is inspired by the V flight formation of the migrating birds and it was shown to perform very well in solving a combinatorial optimization problem, namely the quadratic assignment problem. As analyzed in this study, it has a very good performance in the fraud detection problem also when compared to classical data mining and genetic algorithms. Its performance is further increased by the help of some modified neighborhood definitions and benefit mechanisms.
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A novel and successful credit card fraud detection system Implemented in a Turkish Bank
(IEEE, 2013) Duman, Ekrem; Buyukkaya, A.; Elikucuk, I.; Industrial Engineering; DUMAN, Ekrem
We developed a credit card fraud detection solution for a major bank in Turkey. The study was completed in about three years and the developed system has been in use since February 2013. It had a great impact in the rule based fraud detection process used by the bank. Indeed, while eighty percent of the rules have been eliminated and the number of alerts has been reduced to half, a significant increase in fraud detection has been recorded. As of now the system can catch ninety seven percent of fraud attempts online or, nearly online. The study is interesting in both the formulation of the problem and the algorithms implemented. In fact, we noticed that the standard classification algorithms are not fully suitable for the fraud detection problem (as the cost of every individual false negative can be different from the others), and we looked for alternative methods, especially the meta-heuristics. Among them the newly introduced migrating birds optimization algorithm (MBO) turned out to be superior and was implemented. In addition, during the study a cost sensitive decision tree algorithm was developed and introduced to the literature.
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Solving credit card fraud detection problem by the new metaheuristics migrating birds optimization
(Springer Science+Business Media, 2013) Duman, Ekrem; Elikucuk, I.; Industrial Engineering; DUMAN, Ekrem
Statistical fraud detection problem is a very difficult problem in that there are very few examples of fraud. The great majority of transactions are legitimate. On the other hand, for this binary classification problem the costs of the two types of classification errors (FP=false positive and FN=false negative) are not the same. Thus, the classical data mining algorithms do not fit to the problem exactly. Departing from this fact, we have solved this problem by genetic algorithms and scatter search. Now, we apply the recently developed new metaheuristics algorithm namely the migrating birds optimization algorithm (MBO) to this problem. Results show that it outperforms the former approach. The performance of standard MBO is further increased by the help of some modified benefit mechanisms.