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How can we spot money laundering in large-scale graph-like accounting datasets? How to identify the most suspicious period in a time-evolving accounting graph? What kind of accounts and events should practitioners prioritize under time constraints? To tackle these crucial challenges in accounting and auditing tasks, we propose a flexible system called AutoAudit, which can be valuable for auditors and risk management professionals.
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S. Ranshous, S. Shen, D. Koutra, S. Harenberg, C. Faloutsos, and N. F. Samatova, “Anomaly detection in dynamic networks: A survey,” Wiley Interdisciplinary Reviews: Computational Statistics , vol. 7, no. 3, 2015
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L. Corselli, “Italy: money transfer, money laundering and intermediary liability,” Journal of Financial Crime , 2020
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