BoostSAGE: A Weighted Ensemble Framework for Explainable Financial Fraud Detection

Abstract

Sophisticated machine learning and deep learning architectures often achieve high predictive performance but remain insufficient for high-stakes domains such as financial fraud detection, where stakeholders require both accuracy and trust in the decision-making process. This study proposes a weighted ensemble framework that strategically combines eXtreme Gradient Boosting (XGBoost) and Graph SAmple and aggreGatE (GraphSAGE) models to optimize the precisionrecall trade-off while maintaining interpretability. Our proposed framework, BoostSAGE, leverages complementary strengths of its constituent models. XGBoost provides high precision (94%) through transaction-level feature analysis, while GraphSAGE ensures superior recall (95%) by capturing structural risk signals across transaction-user-merchant relationships. Empirical results indicate that BoostSAGE outperforms constituent models by synthesizing local and relational feature sets. Most notably, the ensemble delivers a 14.97% improvement in AUCPR over the XGBoost baseline. By leveraging historical fraud aggregation within user-centric connections, the model reduces total False Negatives by 44.8% compared to XGBoost, yielding a final F1score of 92.88%. Our results demonstrate that weighted ensemble framework provides a robust and interpretable approach to fraud detection, balancing operational efficiency with stakeholder trust.