An Explainable Ensemble Framework for Ethereum Fraud Detection Using SHAP-Based Interpretations

Abstract

Cryptocurrency fraud on blockchain platforms continues to cause substantial financial losses, creating an urgent need for detection systems that are not only accurate but also interpretable for operational and regulatory use. In this paper, we propose an explainable framework for Ethereum fraud detection integrating an XGBoost ensemble with TreeSHAP. This system achieves high predictive performance (96.3% F1-score, 96.6% recall) while providing model-level transparency via an interactive chatbot interface. Evaluation using fidelity and stability metrics confirms the reliability of the SHAP-based insights, while user-role simulations demonstrate that our structured delivery enhances clarity and actionability over standard visualizations. This work offers a practical, transparent foundation for deploying robust AI in high-risk financial environments without sacrificing accuracy.