An Explainable Machine Learning Framework for Detecting Illicit Bitcoin Wallets with Graph Temporal Features
| Type of publication: | Inproceedings |
| Citation: | |
| Publication status: | Published |
| Volume: | Proceedings of International Summer Conference 2025 |
| Year: | 2025 |
| Month: | June |
| Abstract: | The pseudonymous nature of blockchain transactions poses a significant challenge for identifying fraudulent activity in decentralized financial systems. This study presents a comprehensive framework for classifying Bitcoin wallets as fraudulent or legitimate by integrating multi-source data, graph-based transaction modeling, and machine learning. Our methodology builds upon publicly available datasets—namely Elliptic, Chainabuse, and a curated sample of recent transactions—and integrates structural, temporal, and monetary features extracted from the Bitcoin transaction graph. Through systematic experiments across three distinct labeling scenarios, we demonstrate that ensemble methods such as Random Forests offer strong performance even under label noise, achieving F1-scores up to 0.92. Moreover, an explainability framework grounded, in SHAP values, is used to systematically analyze feature contributions and elucidate behavioral patterns associated with financial fraud. Our approach bridges empirical robustness with forensic insight, contributing a scalable, transparent toolset for blockchain compliance and risk analysis. |
| Keywords: | Bitcoin, Blockchain Analytics, Explainable AI, Financial Fraud, machine learning, Wallet Classification |
| Authors | |
| Added by: | [] |
| Total mark: | 0 |
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