Duman, EkremElikucuk, I.2014-11-252014-11-252013978-3-642-38682-4https://doi.org/10.1007/978-3-642-38682-4_8http://hdl.handle.net/10679/671Due to copyright restrictions, the access to the full text of this article is only available via subscription.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.engrestrictedAccessconferenceObject7903627100032489920000810.1007/978-3-642-38682-4_8Migrating birds optimization algorithmFraudCredit cardsGenetic algorithms2-s2.0-84880057301