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Publicação
Evaluating Gains on Blockchain Analysis for Money Laundering (AML) detection through Machine Learning Operations (MLOps) adoption
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Resumo(s)
The complexity and scale of blockchain transaction data present significant challenges for Anti-Money Laundering (AML) detection, particularly in the absence of labeled ground truth. This thesis explores how Machine Learning Operations (MLOps) practices can improve the design, reproducibility, and performance of AML detection pipelines built upon blockchain data. A modular pipeline is proposed to process real-time USDC token transfers from the Ethereum mainnet using Hyperledger Besu, Apache Kafka, and Neo4j. Transactions are enriched with graph-based features to expose laundering typologies such as smurfing and funneling. Unsupervised learning is applied through Isolation Forests trained on generated synthetic data. MLOps tools - including MLflow for model tracking, Feast for feature management, and Prefect for orchestration - are assessed to enable versioning, monitoring, and automated retraining in response to concept drift. The pipeline’s effectiveness is demonstrated through anomaly detection experiments, and a comparative analysis quantifies the measurable benefits introduced by MLOps adoption. This research contributes a practical framework that bridges data science and production, illustrating how operational maturity enhances the traceability, robustness, and interpretability of AML systems in blockchain contexts.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
Palavras-chave
Blockchain Analytics MLOps Money Laundering Detection Anomaly Detection Graph-based Features