A Powerful Predicting Model for Financial Statement Fraud Based on Optimized XGBoost Ensemble Learning Technique

A Powerful Predicting Model for Financial Statement Fraud Based on Optimized XGBoost Ensemble Learning Technique

This study aims to develop a better Financial Statement Fraud (FSF) detection model by utilizing data from publicly available financial statements of firms in the MENA region. We develop an FSF model using a powerful ensemble technique, the XGBoost (eXtreme Gradient Boosting) algorithm, that helps t...

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Bibliographic Details
Main Authors: Amal Al Ali, Ahmed M. Khedr, Magdi El-Bannany, Sakeena Kanakkayil
Format: Article
Language:English
Published: MDPI AG 2023-02-01
Series:Applied Sciences
Subjects:
financial statement fraud (FSF)
fraud detection
ensemble approach
Middle East and North Africa (MENA)
machine learning (ML)
XGBoost (eXtreme Gradient Boosting)
Online Access:https://www.mdpi.com/2076-3417/13/4/2272

Internet

https://www.mdpi.com/2076-3417/13/4/2272

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