Hide and seek in transaction networks: a multi-agent framework for simulating and detecting money laundering activities
Why this work is in the frame
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Bibliographic record
Abstract
Detecting money laundering within financial networks presents a complex challenge due to the elusive behavior patterns of laundering agents, often resulting in data gaps. In this research, we propose a ‘Multiverse Simulation’ framework using a multi-agent system to generate synthetic datasets for anti-money laundering (AML) training and detection. This framework creates diverse virtual worlds, each with unique parameters to represent varying levels of illicit activity, thus mimicking the dynamics of money laundering and legitimate transactions. Our framework comprises two main types of agents: (1) the Detector, trained to identify laundering signs, and (2) Transaction agents, divided into those involved in laundering and those in legal transactions. These agents interact in a synthetic environment governed by rules that simulate real-world financial behaviors, enabling the generation of complex, realistic data. In the hide-and-seek multiverse simulation, the Detector learns to distinguish between licit and illicit transactions, a process refined by the evolving strategies of transaction agents to avoid detection. This adversarial setup fosters the co-evolution of laundering techniques and detection methods, enhancing system robustness. We demonstrate the efficacy of this approach by pre-training on synthetic cross-bank data, then evaluating with real-world data from the Elliptic dataset. Our results show that transfer learning significantly improves AML system performance, effectively bridging the gap between synthetic and authentic transaction patterns. The ‘Multiverse Simulation’ offers a scalable, dynamic approach to better understand and mitigate the gap between simulation and reality, contributing to more resilient and intelligent AML solutions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it