# Quick survey of graph-based fraud detection methods

arXiv: https://arxiv.org/abs/1910.11299 · HTML: https://mlsanity.com/paper/1910.11299/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2019.

## Built on by (most cited first, 1 of 1)

- [FRACTION: Fraud Explanation Generation via Agentic Graph Context Navigation](https://mlsanity.com/paper/2610.18023/) (2026)

## Abstract (arXiv)

In general, anomaly detection is the problem of distinguishing between normal data samples with well defined patterns or signatures and those that do not conform to the expected profiles. Financial transactions, customer reviews, social media posts are all characterized by relational information. In these networks, fraudulent behaviour may appear as a distinctive graph edge, such as spam message, a node or a larger subgraph structure, such as when a group of clients engage in money laundering schemes. Most commonly, these networks are represented as attributed graphs, with numerical features complementing relational information. We present a survey on anomaly detection techniques used for fraud detection that exploit both the graph structure underlying the data and the contextual information contained in the attributes.