# Identifying Illicit Accounts in Large Scale E-payment Networks -- A Graph Representation Learning Approach

arXiv: https://arxiv.org/abs/1906.05546 · HTML: https://mlsanity.com/paper/1906.05546/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2019.

## Built on by (most cited first, 9 of 9)

- [Identity Inference on Blockchain using Graph Neural Network](https://mlsanity.com/paper/2104.06559/) (2021)
- [Evolve Path Tracer: Early Detection of Malicious Addresses in Cryptocurrency](https://mlsanity.com/paper/2301.05412/) (2023)
- [Blockchain Phishing Scam Detection via Multi-channel Graph Classification](https://mlsanity.com/paper/2108.08456/) (2021)
- [Artificial Intelligence for Web 3.0: A Comprehensive Survey](https://mlsanity.com/paper/2309.09972/) (2023)
- [Effective Illicit Account Detection on Large Cryptocurrency MultiGraphs](https://mlsanity.com/paper/2309.02460/) (2023)
- [From Asset Flow to Status, Action and Intention Discovery: Early Malice Detection in Cryptocurrency](https://mlsanity.com/paper/2309.15133/) (2023)
- [MEGA: Message Passing Neural Networks for Multigraphs with EdGe Attributes](https://mlsanity.com/paper/2412.00241/) (2024)
- [Toward Intention Discovery for Early Malice Detection in Bitcoin](https://mlsanity.com/paper/2209.12001/) (2022)
- [Privacy-Preserving GNN Inference on Financial Transaction Graphs via Homomorphic Encryption](https://mlsanity.com/paper/2610.18054/) (2026)

## Abstract (arXiv)

Rapid and massive adoption of mobile/ online payment services has brought new challenges to the service providers as well as regulators in safeguarding the proper uses such services/ systems. In this paper, we leverage recent advances in deep-neural-network-based graph representation learning to detect abnormal/ suspicious financial transactions in real-world e-payment networks. In particular, we propose an end-to-end Graph Convolution Network (GCN)-based algorithm to learn the embeddings of the nodes and edges of a large-scale time-evolving graph. In the context of e-payment transaction graphs, the resultant node and edge embeddings can effectively characterize the user-background as well as the financial transaction patterns of individual account holders. As such, we can use the graph embedding results to drive downstream graph mining tasks such as node-classification to identify illicit accounts within the payment networks. Our algorithm outperforms state-of-the-art schemes including GraphSAGE, Gradient Boosting Decision Tree and Random Forest to deliver considerably higher accuracy (94.62% and 86.98% respectively) in classifying user accounts within 2 practical e-payment transaction datasets. It also achieves outstanding accuracy (97.43%) for another biomedical entity identification task while using only edge-related information.