Hybrid Unsupervised Machine Learning for Insurance Fraud Detection: PCA-XGBoost-LOF and Isolation Forest

Hybrid Unsupervised Machine Learning for Insurance Fraud Detection: PCA-XGBoost-LOF and Isolation Forest

Insurance fraud poses a significant threat to the financial stability of insurance companies, resulting in substantial economic losses. To combat this issue, this study proposes a novel unsupervised machine learning hybrid algorithm, integrating Principal Component Analysis (PCA), Extreme Gradient B...

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Bibliographic Details
Main Authors: Natsai Chapwanya, Karikoga Norman Gorejena
Format: Article
Language:English
Published: Informatics Department, Faculty of Computer Science Bina Darma University 2025-03-01
Series:Journal of Information Systems and Informatics
Subjects:
insurance fraud detection; hybrid machine learning; unsupervised learning; anomaly detection; principal component analysis (pca)
Online Access:https://journal-isi.org/index.php/isi/article/view/958
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https://journal-isi.org/index.php/isi/article/view/958

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