Diagnosis of internal frauds using extreme gradient boosting model optimized with genetic algorithm in retailing

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dc.contributor.authorDemirdelen, Aytek
dc.contributor.authorVardarlıer, Pelin
dc.contributor.authorMeral, Yurdagül
dc.contributor.authorÖzcan, Tuncay
dc.date.accessioned2025-07-24T09:07:50Z
dc.date.available2025-07-24T09:07:50Z
dc.date.issued2024
dc.departmentİstanbul Medipol Üniversitesi, İşletme ve Yönetim Bilimleri Fakültesi, İnsan Kaynakları Yönetimi Bölümü
dc.departmentİstanbul Medipol Üniversitesi, Sosyal Bilimler Enstitüsü, İşletme Yönetimi Ana Bilim Dalı
dc.description.abstractFraud is one of the most vital problems that can lead to a loss of organizational reputation, assets and culture. It is beneficial for companies to anticipate possible fraud in order to protect both culture and company assets. The aim of this study is to provide a fraud detection model using classification and optimization algorithms. For this purpose, this study proposes a novel hybrid model called XGBoost-GA to enhance the prediction quality for cashier fraud detection in retailing. In the proposed model, the genetic algorithm (GA) is used to optimize the parameters of extreme gradient boosting (XGBoost) model. The proposed XGBoost-GA model is compared with XGBoost, logistic regression (LR), naive bayes (NB) and k-nearest neighbor (kNN)algorithms. The performance comparison is presented with a case study with the actual data taken from a grocery retailer in Turkey. Numerical results showed that the proposed hybrid XGBoost-GA model produces higher accuracy, recall, precision and F-measure than other classification algorithms. In this context, the use of proposed model in fraud detection will be beneficial for companies to use their resources effectively. Classification algorithms will also accelerate organizations in terms of detecting the possible damage of fraud to company assets before it grows.
dc.identifier.citationDemirdelen, A., Vardarlıer, P., Meral, Y. ve Özcan, T. (2024). Diagnosis of internal frauds using extreme gradient boosting model optimized with genetic algorithm in retailing. Acta Infologica, 8(1), 60-70. http://dx.doi.org/10.26650/acin.1475658
dc.identifier.doi10.26650/acin.1475658
dc.identifier.endpage70
dc.identifier.issn2602-3563
dc.identifier.issue1
dc.identifier.startpage60
dc.identifier.urihttp://dx.doi.org/10.26650/acin.1475658
dc.identifier.urihttps://hdl.handle.net/20.500.12511/13022
dc.identifier.volume8
dc.identifier.wosWOS:001318386200006
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.institutionauthorDemirdelen, Aytek
dc.institutionauthorVardarlıer, Pelin
dc.institutionauthorMeral, Yurdagül
dc.institutionauthorid0000-0002-6005-4604
dc.institutionauthorid0000-0002-5101-6841
dc.institutionauthorid0000-0001-9244-1994
dc.language.isoen
dc.relation.ispartofActa Infologica
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsAttributionNonCommercial 4.0 International License
dc.subjectFraud Detection
dc.subjectRetailing
dc.subjectMachine Learning
dc.subjectExtreme Gradient Boosting
dc.subjectGenetic Algorithm
dc.titleDiagnosis of internal frauds using extreme gradient boosting model optimized with genetic algorithm in retailing
dc.typeArticle

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