Point of sale Fraud detection methods via machine learning

Name: Point of sale Fraud detection methods via machine learning
Author: Begen, E., Sayan, İ. U., Bayrak, A. T., Yıldız, Olcay Taner

İsim	Point of sale Fraud detection methods via machine learning
Yazar	Begen, E., Sayan, İ. U., Bayrak, A. T., Yıldız, Olcay Taner
Basım Tarihi:	2023
Basım Yeri	- IEEE
Konu	Lgbm, Point of sale fraud detection, Resampling, Unbalanced data, Xgboost
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	979-835033890-4
Kayıt Numarası	d34d2ef8-0e8e-4722-81f9-e0ed19680476
Lokasyon	Computer Science
Tarih	2023
Örnek Metin	Restaurant cash registers frequently experience fraudulent transactions, leading to substantial financial losses for operators. Despite several methods aimed at preventing fraud at the cash register, addressing this issue remains an ongoing concern. In this study, machine learning methods are used to detect fraudulent transactions at the cash register in fast-food restaurants. By using POS logs, transactions in restaurants are recorded and these logs are analyzed to detect fraudulent transactions on an unbalanced dataset. Random forest, XGBoost and LGBM algorithms are used in the study and different resampling techniques (ADASYN etc.) are applied to improve the performance of these algorithms. In addition, it is aimed to find the best parameters with the randomized search method. In conclusion, this study offers a solution for detecting fraudulent transactions at the cash register in fast-food restaurants. The results of the study are promising in its current state.
DOI	10.1109/INISTA59065.2023.10310515

Kaynağa git Özyeğin Üniversitesi

Aramaya Dön

Özyeğin Üniversitesi

Kaynağa git

Point of sale Fraud detection methods via machine learning

Yazar Begen, E., Sayan, İ. U., Bayrak, A. T., Yıldız, Olcay Taner

Basım Tarihi 2023

Basım Yeri - IEEE

Konu Lgbm, Point of sale fraud detection, Resampling, Unbalanced data, Xgboost

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 979-835033890-4

Kayıt Numarası d34d2ef8-0e8e-4722-81f9-e0ed19680476

Lokasyon Computer Science

Tarih 2023

Örnek Metin Restaurant cash registers frequently experience fraudulent transactions, leading to substantial financial losses for operators. Despite several methods aimed at preventing fraud at the cash register, addressing this issue remains an ongoing concern. In this study, machine learning methods are used to detect fraudulent transactions at the cash register in fast-food restaurants. By using POS logs, transactions in restaurants are recorded and these logs are analyzed to detect fraudulent transactions on an unbalanced dataset. Random forest, XGBoost and LGBM algorithms are used in the study and different resampling techniques (ADASYN etc.) are applied to improve the performance of these algorithms. In addition, it is aimed to find the best parameters with the randomized search method. In conclusion, this study offers a solution for detecting fraudulent transactions at the cash register in fast-food restaurants. The results of the study are promising in its current state.

DOI 10.1109/INISTA59065.2023.10310515