  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. To sum
up, there are four major advantages of the proposed system: (a) "Smurfing"
Detection, spots nearly 100% of injected money laundering transactions
automatically in real-world datasets. (b) Attention Routing, attends to the
most suspicious part of time-evolving graphs and provides an intuitive
interpretation. (c) Insight Discovery, identifies similar month-pair patterns
proved by "success stories" and patterns following Power Laws in log-logistic
scales. (d) Scalability and Generality, ensures AutoAudit scales linearly and
can be easily extended to other real-world graph datasets. Experiments on
various real-world datasets illustrate the effectiveness of our method. To
facilitate reproducibility and accessibility, we make the code, figure, and
results public at https://github.com/mengchillee/AutoAudit.
