Acta Infologica

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DOI :10.26650/acin.1475658   IUP :10.26650/acin.1475658    Full Text (PDF)

Diagnosis of Internal Frauds using Extreme Gradient Boosting Model Optimized with Genetic Algorithm in Retailing

Aytek Demirdelen, Pelin Vardarlıer, Yurdagül Meral, Tuncay Özcan

Fraud 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.

Keywords: Fraud Detection, Retailing, Machine Learning, Extreme Gradient Boosting, Genetic Algorithm

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APA

Demirdelen, A., Vardarlıer, P., Meral, Y., & Özcan, T. (2024). Diagnosis of Internal Frauds using Extreme Gradient Boosting Model Optimized with Genetic Algorithm in Retailing. Acta Infologica, 8(1), 60-70. https://doi.org/10.26650/acin.1475658


AMA

Demirdelen A, Vardarlıer P, Meral Y, Özcan T. Diagnosis of Internal Frauds using Extreme Gradient Boosting Model Optimized with Genetic Algorithm in Retailing. Acta Infologica. 2024;8(1):60-70. https://doi.org/10.26650/acin.1475658


ABNT

Demirdelen, A.; Vardarlıer, P.; Meral, Y.; Özcan, T. Diagnosis of Internal Frauds using Extreme Gradient Boosting Model Optimized with Genetic Algorithm in Retailing. Acta Infologica, [Publisher Location], v. 8, n. 1, p. 60-70, 2024.


Chicago: Author-Date Style

Demirdelen, Aytek, and Pelin Vardarlıer and Yurdagül Meral and Tuncay Özcan. 2024. “Diagnosis of Internal Frauds using Extreme Gradient Boosting Model Optimized with Genetic Algorithm in Retailing.” Acta Infologica 8, no. 1: 60-70. https://doi.org/10.26650/acin.1475658


Chicago: Humanities Style

Demirdelen, Aytek, and Pelin Vardarlıer and Yurdagül Meral and Tuncay Özcan. “Diagnosis of Internal Frauds using Extreme Gradient Boosting Model Optimized with Genetic Algorithm in Retailing.” Acta Infologica 8, no. 1 (Nov. 2024): 60-70. https://doi.org/10.26650/acin.1475658


Harvard: Australian Style

Demirdelen, A & Vardarlıer, P & Meral, Y & Özcan, T 2024, 'Diagnosis of Internal Frauds using Extreme Gradient Boosting Model Optimized with Genetic Algorithm in Retailing', Acta Infologica, vol. 8, no. 1, pp. 60-70, viewed 22 Nov. 2024, https://doi.org/10.26650/acin.1475658


Harvard: Author-Date Style

Demirdelen, A. and Vardarlıer, P. and Meral, Y. and Özcan, T. (2024) ‘Diagnosis of Internal Frauds using Extreme Gradient Boosting Model Optimized with Genetic Algorithm in Retailing’, Acta Infologica, 8(1), pp. 60-70. https://doi.org/10.26650/acin.1475658 (22 Nov. 2024).


MLA

Demirdelen, Aytek, and Pelin Vardarlıer and Yurdagül Meral and Tuncay Özcan. “Diagnosis of Internal Frauds using Extreme Gradient Boosting Model Optimized with Genetic Algorithm in Retailing.” Acta Infologica, vol. 8, no. 1, 2024, pp. 60-70. [Database Container], https://doi.org/10.26650/acin.1475658


Vancouver

Demirdelen A, Vardarlıer P, Meral Y, Özcan T. Diagnosis of Internal Frauds using Extreme Gradient Boosting Model Optimized with Genetic Algorithm in Retailing. Acta Infologica [Internet]. 22 Nov. 2024 [cited 22 Nov. 2024];8(1):60-70. Available from: https://doi.org/10.26650/acin.1475658 doi: 10.26650/acin.1475658


ISNAD

Demirdelen, Aytek - Vardarlıer, Pelin - Meral, Yurdagül - Özcan, Tuncay. “Diagnosis of Internal Frauds using Extreme Gradient Boosting Model Optimized with Genetic Algorithm in Retailing”. Acta Infologica 8/1 (Nov. 2024): 60-70. https://doi.org/10.26650/acin.1475658



TIMELINE


Submitted01.05.2024
Accepted14.05.2024
Published Online03.06.2024

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