Research Article


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 DemirdelenPelin VardarlıerYurdagül MeralTuncay 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.


PDF View

References

  • Askari, S. M. S., & Hussain, M. A. (2020). IFDTC4. 5: Intuitionistic fuzzy logic based decision tree for E-transactional fraud detection. Journal of Information Security and Applications, 52, 102469. google scholar
  • Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794). google scholar
  • Chen, Z., Jiang, F., Cheng, Y., Gu, X., Liu, W., & Peng, J. (2018, January). XGBoost classifier for DDoS attack detection and analysis in SDN-based cloud. In 2018 IEEE international conference on big data and smart computing (bigcomp) (pp. 251-256). IEEE. google scholar
  • Erol, S. (2016). Hile denetiminde proaktif yaklaşımlar (Master’s thesis, Sosyal Bilimler Enstitüsü). google scholar
  • ESMERAY, A. (2018). BİLİŞİM TEKNOLOJİSİNDEKİ GELİŞMELERİN MUHASEBE DENETİMİNE KATKISI. Muhasebe Bilim Dünyası Dergisi, 20, 294-309. google scholar
  • Gee, J., & Button, M. (2019). The financial cost of fraud 2019: The latest data from around the world. google scholar
  • Hanagandi, V., Dhar, A., & Buescher, K. (1996, March). Density-based clustering and radial basis function modeling to generate credit card fraud scores. In IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr) (pp. 247-251). IEEE. google scholar
  • Holland, J. H. (1975). Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence. google scholar
  • Huang, Z., Zheng, H., Li, C., & Che, C. (2024). Application of Machine Learning-Based K-Means Clustering for Financial Fraud Detection. Academic Journal of Science and Technology, 10(1), 33-39. google scholar
  • Mahmoudi, N., & Duman, E. (2015). Detecting credit card fraud by modified Fisher discriminant analysis. Expert Systems with Applications, 42(5), 2510-2516. google scholar
  • Nadim, A. H., Sayem, I. M., Mutsuddy, A., & Chowdhury, M. S. (2019, December). Analysis of machine learning techniques for credit card fraud detection. In 2019 International Conference on Machine Learning and Data Engineering (iCMLDE) (pp. 42-47). IEEE. google scholar
  • Niu, X., Wang, L., & Yang, X. (2019). A comparison study of credit card fraud detection: Supervised versus unsupervised. arXiv preprint arXiv:1904.10604. google scholar
  • Parmar, J., Patel, A., & Savsani, M. (2020). Credit card fraud detection framework-a machine learning perspective. International Journal of Scientific Research in Science and Technology, 7(6), 431-435. google scholar
  • Pehlivanli, D., Eken, S., & AYAN, E. B. (2019). Detection of fraud risks in retailing sector using MLP and SVM techniques. Turkish Journal of Electrical Engineering and Computer Sciences, 27(5), 3633-3647. google scholar
  • Perols, J. (2011). Financial statement fraud detection: An analysis of statistical and machine learning algorithms. Auditing: A Journal of Practice & Theory, 30(2), 19-50. google scholar
  • Renjith, S. (2018). Detection of fraudulent sellers in online marketplaces using support vector machine approach. arXiv preprint arXiv:1805.00464. google scholar
  • Roseline, J. F., Naidu, G. B. S. R., Pandi, V. S., alias Rajasree, S. A., & Mageswari, N. (2022). Autonomous credit card fraud detection using machine learning approach. Computers and Electrical Engineering, 102, 108132. google scholar
  • Sahin, Y., & Duman, E. (2011, March). Detecting credit card fraud by decision trees and support vector machines. In Proceedings of the International MultiConference of Engineers and Computer Scientists (Vol. 1, pp. 1-6). google scholar
  • Seyedhossein, L., & Hashemi, M. R. (2010, December). Mining information from credit card time series for timelier fraud detection. In 2010 5th International Symposium on Telecommunications (pp. 619-624). IEEE. google scholar
  • Shen, A., Tong, R., & Deng, Y. (2007, June). Application of classification models on credit card fraud detection. In 2007 International conference on service systems and service management (pp. 1-4). IEEE. google scholar
  • Shukur, H. A., & Kurnaz, S. (2019). Credit card fraud detection using machine learning methodology. International Journal of Computer Science and Mobile Computing, 8(3), 257-260. google scholar
  • Van Vlasselaer, V., Bravo, C., Caelen, O., Eliassi-Rad, T., Akoglu, L., Snoeck, M., & Baesens, B. (2015). APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions. Decision support systems, 75, 38-48. google scholar
  • Varmedja, D., Karanovic, M., Sladojevic, S., Arsenovic, M., & Anderla, A. (2019, March). Credit card fraud detection-machine learning methods. In 2019 18th International Symposium INFOTEH-JAHORINA (INFOTEH) (pp. 1-5). IEEE. google scholar
  • Walke, A. (2019). Comparison of supervised and unsupervised fraud detection. In Advances in Data Science, Cyber Security and IT Applications: First International Conference on Computing, ICC 2019, Riyadh, Saudi Arabia, December 10-12, 2019, Proceedings, Part 11 (pp. 8-14). Springer International Publishing. google scholar
  • Yi, Z., Cao, X., Pu, X., Wu, Y., Chen, Z., Khan, A. T., ... & Li, S. (2023). Fraud detection in capital markets: A novel machine learning approach. Expert Systems with Applications, 231, 120760. google scholar

Citations

Copy and paste a formatted citation or use one of the options to export in your chosen format


EXPORT



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 (Jul. 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 25 Jul. 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 (25 Jul. 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]. 25 Jul. 2024 [cited 25 Jul. 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 (Jul. 2024): 60-70. https://doi.org/10.26650/acin.1475658



TIMELINE


Submitted01.05.2024
Accepted14.05.2024
Published Online03.06.2024

LICENCE


Attribution-NonCommercial (CC BY-NC)

This license lets others remix, tweak, and build upon your work non-commercially, and although their new works must also acknowledge you and be non-commercial, they don’t have to license their derivative works on the same terms.


SHARE




Istanbul University Press aims to contribute to the dissemination of ever growing scientific knowledge through publication of high quality scientific journals and books in accordance with the international publishing standards and ethics. Istanbul University Press follows an open access, non-commercial, scholarly publishing.