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

Aytek Demirdelen; Pelin Vardarlıer; Yurdagül Meral; Tuncay Özcan

Makale detayları

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

Yazarlar:

Aytek Demirdelen | Pelin Vardarlıer | Yurdagül Meral | Tuncay Özcan

Yayıncı:

Istanbul University

Ana Başlıklar:

Yazılım Mühendisliği (Diğer)

Dergi:

Acta Infologica Vol.8 Issue 1, pp.60-70

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001323767

pISSN:

2602-3563

Anahtar kelimeler:

Özet:

 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.

 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.

 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.

 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.

Makale detayları

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

Yazarlar:

Aytek Demirdelen | Pelin Vardarlıer | Yurdagül Meral | Tuncay Özcan

Yayıncı:

Istanbul University

Ana Başlıklar:

Yazılım Mühendisliği (Diğer)

Dergi:

Acta Infologica Vol.8 Issue 1, pp.60-70

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001323767

pISSN:

2602-3563

Fraud Detection

Retailing

Machine Learning

Extreme Gradient Boosting

Genetic Algorithm

Sayı

Makale detayları

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

Yazarlar: Aytek Demirdelen | Pelin Vardarlıer | Yurdagül Meral | Tuncay Özcan

Yayıncı: Istanbul University

Ana Başlıklar: Yazılım Mühendisliği (Diğer)

Dergi: Acta Infologica Vol.8 Issue 1, pp.60-70

Belge Tipi: Makale

Yayın Tarihi: 6/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001323767

pISSN: 2602-3563

Fraud Detection

Retailing

Machine Learning

Extreme Gradient Boosting

Genetic Algorithm

Sayı

Yazarlar:

Aytek Demirdelen | Pelin Vardarlıer | Yurdagül Meral | Tuncay Özcan

Yayıncı:

Istanbul University

Ana Başlıklar:

Yazılım Mühendisliği (Diğer)

Dergi:

Acta Infologica Vol.8 Issue 1, pp.60-70

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001323767

pISSN:

2602-3563