Fraud Detection in Cryptocurrency Transactions Using Deep Learning

Abstract

Cryptocurrency’s promise of open, programmable finance has been shadowed by an evolving spectrum of fraud—investment scams, phishing, ransomware monetization, and money-laundering pipelines that hop across chains and jurisdictions. Traditional rule-based monitoring struggles with scale, concept drift, and adversarial obfuscation (mixers, peel chains, cross-chain bridges). This manuscript synthesizes the state of the art and proposes an end-to-end deep learning framework for fraud detection that blends graph neural networks (GNNs) on transaction graphs with temporal sequence models, representation learning for wallets and entities, and risk-aware active learning to leverage sparse labels. We ground the discussion in public datasets (e.g., Elliptic1/Elliptic2, BitcoinHeist, XBlock-ETH) and up-to-date industry intelligence (Chainalysis, TRM Labs) and map the methodology to regulatory expectations (FATF’s standards and the Travel Rule). We describe a modular pipeline covering data engineering, graph construction, feature learning, semi-supervised training, evaluation (ROC-AUC, PR-AUC, precision@k), and explainability (counterfactual traces, subgraph rationales). Results summarized from recent literature show that GNNs and temporal graph transformers generally outperform shallow models and static heuristics, especially when augmented with edge-time features and neighborhood motif learning. We conclude with deployment guidance—human-in-the-loop triage, concept-drift monitoring, and privacy-preserving analytics—and outline limitations (label scarcity, feedback loops, evasion tactics) and future work (federated graph learning, causal inference on chain, and cross-chain entity resolution).