# Graph Computing for Financial Crime and Fraud Detection: Trends, Challenges and Outlook

arXiv: https://arxiv.org/abs/2103.03227 · HTML: https://mlsanity.com/paper/2103.03227/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 127 references that are in the map.
- First posted on arXiv 2 Mar 2021.

## Builds on (most cited first, 30 of 36)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Practical Black-Box Attacks against Machine Learning](https://mlsanity.com/paper/1602.02697/) (2016)
- [GNNExplainer: Generating Explanations for Graph Neural Networks](https://mlsanity.com/paper/1903.03894/) (2019)
- [Neural Combinatorial Optimization with Reinforcement Learning](https://mlsanity.com/paper/1611.09940/) (2016)
- [Temporal Graph Networks for Deep Learning on Dynamic Graphs](https://mlsanity.com/paper/2006.10637/) (2020)
- [EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs](https://mlsanity.com/paper/1902.10191/) (2019)
- [Adversarial Attacks on Neural Networks for Graph Data](https://mlsanity.com/paper/1805.07984/) (2018)
- [Deep Learning on Graphs: A Survey](https://mlsanity.com/paper/1812.04202/) (2018)
- [DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning](https://mlsanity.com/paper/1707.06690/) (2017)
- [Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters](https://mlsanity.com/paper/2008.08692/) (2020)
- [Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning](https://mlsanity.com/paper/1804.00308/) (2018)
- [Explainability Techniques for Graph Convolutional Networks](https://mlsanity.com/paper/1905.13686/) (2019)
- [Machine Learning on Graphs: A Model and Comprehensive Taxonomy](https://mlsanity.com/paper/2005.03675/) (2020)
- [Streaming Graph Neural Networks](https://mlsanity.com/paper/1810.10627/) (2018)
- [Certifiable Robustness and Robust Training for Graph Convolutional Networks](https://mlsanity.com/paper/1906.12269/) (2019)
- [Transferring Robustness for Graph Neural Network Against Poisoning Attacks](https://mlsanity.com/paper/1908.07558/) (2019)
- [Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use case](https://mlsanity.com/paper/1910.07421/) (2019)
- [Scalable Graph Learning for Anti-Money Laundering: A First Look](https://mlsanity.com/paper/1812.00076/) (2018)
- [Clustering attributed graphs: models, measures and methods](https://mlsanity.com/paper/1501.01676/) (2015)
- [Deep Learning in Finance](https://mlsanity.com/paper/1602.06561/) (2016)
- [How Robust Are Graph Neural Networks to Structural Noise?](https://mlsanity.com/paper/1912.10206/) (2019)
- [AutoAudit: Mining Accounting and Time-Evolving Graphs](https://mlsanity.com/paper/2011.00447/) (2020)

## Built on by (most cited first, 8 of 8)

- [A Review on Graph Neural Network Methods in Financial Applications](https://mlsanity.com/paper/2111.15367/) (2021)
- [Graph Neural Networks for Financial Fraud Detection: A Review](https://mlsanity.com/paper/2411.05815/) (2024)
- [Towards Representation Identical Privacy-Preserving Graph Neural Network via Split Learning](https://mlsanity.com/paper/2107.05917/) (2021)
- [A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection](https://mlsanity.com/paper/2302.03654/) (2023)
- [Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental Evaluation](https://mlsanity.com/paper/2405.19383/) (2024)
- [Privately Learning from Graphs with Applications in Fine-tuning Large Language Models](https://mlsanity.com/paper/2410.08299/) (2024)
- [Privacy-Preserving Graph-Based Machine Learning with Fully Homomorphic Encryption for Collaborative Anti-Money Laundering](https://mlsanity.com/paper/2411.02926/) (2024)
- [Advances in Continual Graph Learning for Anti-Money Laundering Systems: A Comprehensive Review](https://mlsanity.com/paper/2503.24259/) (2025)

## Abstract (arXiv)

The rise of digital payments has caused consequential changes in the financial crime landscape. As a result, traditional fraud detection approaches such as rule-based systems have largely become ineffective. AI and machine learning solutions using graph computing principles have gained significant interest in recent years. Graph-based techniques provide unique solution opportunities for financial crime detection. However, implementing such solutions at industrial-scale in real-time financial transaction processing systems has brought numerous application challenges to light. In this paper, we discuss the implementation difficulties current and next-generation graph solutions face. Furthermore, financial crime and digital payments trends indicate emerging challenges in the continued effectiveness of the detection techniques. We analyze the threat landscape and argue that it provides key insights for developing graph-based solutions.