GraphALM: Active Learning for Detecting Money Laundering Transactions on Blockchain Networks
Bibliographic record
Abstract
In recent years, the decentralization, anonymity, and cross-border capabilities of cryptocurrencies have significantly increased their use in money laundering activities. In an era rigorously regulated by enhanced global anti-money laundering (AML) measures, designing an efficient approach to detect potential money laundering in blockchain is essential. In this research, we present GraphALM, an active learning model based on reinforcement learning, aiming to improve the detection performance of money laundering activities in blockchain transactions. This model addresses the challenge of efficiently sampling training data batches to identify illicit activities within the vast and complex dataset of Bitcoin transactions. Additionally, we have constructed a new Realistic and Demand (RD) Bitcoin dataset, augmented with feature uncertainty, to better simulate real-world scenarios. The results of our experiments demonstrate the effectiveness, robustness, and explainability of our proposed model, contributing to the application of active learning strategies in the field of financial regulation within blockchain networks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".