Explainable Machine Learning Models for Risk Assessment in Blockchain Payment Gateways

Abstract

Emerging blockchain payment gateways have facilitated worldwide financial systems with unprecedented efficiency, transparency, and decentralization. Yet, increasingly, such platforms become susceptible to complex financial risks such as fraud at various scales, double-spending, Sybil attacks, and illegal access. The rule-based approaches that were traditionally implemented are no longer adequate to keep up with the evolving threat landscape of decentralized finance (DeFi). This, therefore, serves to strengthen the stance for considering ML models for at least real-time transaction analysis and fraud detection. Even with many models offering good prediction capabilities, the lack of transparency raises serious concerns about issues of interpretability and compliance—especially in environments that are financially regulated. The paper thus delves into the incorporation of explainable machine learning (XML) techniques in blockchain payment risk assessment frameworks. Using model-agnostic tools such as SHAP (SHapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) and combining them with very high-end models such as XGBoost and LightGBM, we create interpretable frameworks that enable stakeholders to understand, trust, and verify the risk classifications issued. Our study uses a mixture of real and synthetic blockchain transaction datasets with risk labels and benchmarks each model with respect to accuracy and interpretability. Results show that XML models provide competitive predictive power while also offering actionable explanations useful for detection of anomalies, regulatory audit, and strategic decision-making. We believe that explainable ML is not just achievable but also an absolute prerequisite for sustainable and compliant risk management in blockchain financial infrastructures