Enhancing Fraud Detection in Financial Transactions using LightGBM and Random Forest

In recent years, the frequency and complexity of financial fraud have been rising and have become an urgent challenge for the global financial system. Traditional rule-based detection methods struggle to cope with new types of fraud, especially in terms of real-time detection, generalization abilit...

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Autors principals:	Wan-Ping Khor, Kah-Ong Michael Goh, Check-Yee Law, Connie Tee, Yong-Wee Sek, Riasat Khan
Format:	Artigo
Idioma:	Inglês
Publicat:	MMU Press 2026-02-01
Col·lecció:	Journal of Informatics and Web Engineering
Matèries:	Financial Fraud Detection Machine Learning LightGBM Random Forest Hybrid Model Imbalanced Dataset
Accés en línia:	https://journals.mmupress.com/index.php/jiwe/article/view/2127
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