AutoAudit: Mining Accounting and Time-Evolving Graphs

December 10, 2020

Meng-Chieh Lee, National Chiao Tung University
Yue Zhao, Carnegie Mellon University
Aluna Wang, Carnegie Mellon University
Pierre Jinghong Liang, Carnegie Mellon University
Leman Akoglu, Carnegie Mellon University
Vincent S. Tseng, National Chiao Tung University
Christos Faloutsos, Carnegie Mellon University

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.