Dynamic Rule Optimization with Explainable Machine Learning in Real-Time Finacial Fraud Detection
DOI:
https://doi.org/10.15662/IJEETR.2026.0802032Keywords:
Financial Fraud Detection, Explainable AI, Machine Learning, XGBoost, Rule Optimization, Hybrid Detection SystemAbstract
financial fraud has become one of the most critical challenges in modern digital banking systems. Traditional rule-based fraud detection systems are transparent but fail to adapt to new fraud patterns, while machine learning models provide high detection accuracy but lack explainability. This paper proposes DROX-FD (Dynamic Rule Optimization with Explainable Fraud Detection), a hybrid framework that combines dynamic rule generation, machine learning, and explainable artificial intelligence
The system automatically generates fraud detection rules using statistical pattern analysis and dynamically updates rule confidence through reinforcement learning. When rule confidence is insufficient, the system invokes an XGBoost-based machine learning model for deeper analysis. To ensure transparency, SHAP-based explainable AI techniques are used to provide human-readable explanations for fraud predictions. The proposed framework supports real-time transaction monitoring and provides a comprehensive dashboard for rule governance, fraud alerts, and system performance evaluation. Experimental analysis demonstrates that the proposed system improves detection accuracy while maintaining explainability and operational efficiency
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