Integrating fraud detection systems, particularly those using AI-driven anomaly detection can enhance the accountability of blockchain networks. By continuously monitoring transaction data, these systems can identify patterns indicative of fraudulent activities, enabling timely intervention and mitigation of potential threats. A study titled “Detecting Anomalies in Blockchain Transactions using Machine Learning Classifiers and Explainability Analysis” explores the application of machine learning models to detect anomalous transactions within blockchain networks. The researchers integrated eXplainable Artificial Intelligence (XAI) techniques with tree-based ensemble classifiers to enhance the interpretability of the detection process. Their findings demonstrate that employing such AI-driven methods significantly improves the identification of suspicious transactions, thereby bolstering the overall security of blockchain systems.
