# AutoAudit: Mining Accounting and Time-Evolving Graphs

arXiv: https://arxiv.org/abs/2011.00447 · HTML: https://mlsanity.com/paper/2011.00447/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2020.

## Built on by (most cited first, 15 of 15)

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- [Model Agnostic Hybrid Sharding For Heterogeneous Distributed Inference](https://mlsanity.com/paper/2407.19775/) (2024)
- [GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money Laundering](https://mlsanity.com/paper/2506.04292/) (2025)
- [FRAUDGUESS: Spotting and Explaining New Types of Fraud in Million-Scale Financial Data](https://mlsanity.com/paper/2509.15493/) (2025)
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## Abstract (arXiv)

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.