Graph-Based Fraud Detection with Optimized Features and Class Balance

  • Anisa Nur Azizah Universitas Wijaya Putra
  • Alven Safik Ritonga Universitas Wijaya Putra
  • Suryo Atmojo Universitas Wijaya Putra
  • Nurwahyudi Widhiyanta Universitas Wijaya Putra
  • Suzana Dewi Universitas Wijaya Putra
  • M Harist Murdani Universitas Wijaya Putra
  • Mamik Usniyah Sari Universitas Wijaya Putra
Keywords: Fraud Detection, GNN, Feature Selection, Class Balance

Abstract

The increasing use of digital transactions also elevates the risk of fraud, particularly in credit card transactions. Fraud detection poses a challenge due to the highly imbalanced nature of the data and the complexity of relationships among entities. This study proposes a GNN-based approach, integrated with feature selection techniques and class imbalance handling through class weighting based on data distribution. Feature selection was performed using two methods: Correlation-based Feature Selection (CFS) and Random Forest Feature Importance, to obtain the most relevant features. Experimental results show that the combination of Random Forest feature selection and class weighting yielded the highest F1 Score, despite a slight decrease in accuracy. This indicates that feature selection and class weighting strategies can improve the model's ability to detect rare fraudulent transactions. This approach contributes to the development of more accurate and adaptive fraud detection systems in digital transaction environments.

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Published
2025-08-02
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