Integrated Financial Fraud Detection: Ensemble Learning, Graph Neural Networks, and Advanced Fraud Categorization

Abstract

Financial fraud continues to pose a significant threat to the integrity of digital financial systems, necessitating robust and adaptive detection frameworks. In this research, we proposed a comprehensive machine learning based pipeline for the categorization and detection of financial fraud. Our approach inte-grates extensive exploratory data analysis (EDA), a hybrid data balancing strategy combining SMOTE and Edited Nearest Neighbors(ENN), and a nov-el fraud categorization scheme based on transaction meta data and user be-havior. To tackle the evolving nature of fraudulent activities, we introduce a hybrid detection model that fuses feature-based ensemble learning (XGBoost, LightGBM, CatBoost) with graph based neural networks (GNNs) that capture complex inter-entity relationships and temporal patterns. The ensemble model ensures high accuracy and interpretability through SHAP-based feature importance, while the GNN component excels in detecting col-laborative fraud schemes via attention mechanisms and heterogeneous graph structures. Final predictions are derived using a decision-level fusion strategy, combining the strengths of both modeling paradigms. Experi-mental results demonstrate that our integrated framework significantly im-proves fraud detection performance across multiple metrics, offering a scala-ble and explainable solution suitable for deployment in real-world financial ecosystems.