mlsanityData to 14 Oct 2026

Financial Crime & Fraud Detection Using Graph Computing: Application Considerations & Outlook

arXiv 2103.01854 ↗First posted 2 Mar 2021cs.LGcited by 4

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Abstract (arXiv)

In recent years, the unprecedented growth in digital payments fueled consequential changes in fraud and financial crimes. In this new landscape, traditional fraud detection approaches such as rule-based engines have largely become ineffective. AI and machine learning solutions using graph computing principles have gained significant interest. Graph neural networks and emerging adaptive solutions provide compelling opportunities for the future of fraud and financial crime detection. However, implementing the graph-based solutions in financial transaction processing systems has brought numerous obstacles and application considerations to light. In this paper, we overview the latest trends in the financial crimes landscape and discuss the implementation difficulties current and emerging graph solutions face. We argue that the application demands and implementation challenges provide key insights in developing effective solutions.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 27 of its 87 references that are in the map. First posted on arXiv 2 Mar 2021.

Builds on

27 of 87 references.

References · 27

  1. 2017Graph Attention Networkscited by 8,923
  2. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  3. 2013Intriguing properties of neural networkscited by 5,155
  4. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  5. 2018How Powerful are Graph Neural Networks?cited by 4,393
  6. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  7. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,576
  8. 2014DeepWalk: Online Learning of Social Representationscited by 1,547
  9. 2018Relational inductive biases, deep learning, and graph networkscited by 1,360
  10. 2016Practical Black-Box Attacks against Machine Learningcited by 903
  11. 2016Neural Combinatorial Optimization with Reinforcement Learningcited by 608
  12. 2020Temporal Graph Networks for Deep Learning on Dynamic Graphscited by 530
15 more
  1. 2019EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphscited by 447
  2. 2018Adversarial Attacks on Neural Networks for Graph Datacited by 397
  3. 2018Deep Learning on Graphs: A Surveycited by 393
  4. 2017DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoningcited by 266
  5. 2020Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsterscited by 250
  6. 2018Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learningcited by 225
  7. 2019Explainability Techniques for Graph Convolutional Networkscited by 161
  8. 2020Machine Learning on Graphs: A Model and Comprehensive Taxonomycited by 156
  9. 2018Streaming Graph Neural Networkscited by 91
  10. 2019Transferring Robustness for Graph Neural Network Against Poisoning Attackscited by 58
  11. 2019Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use casecited by 53
  12. 2018Scalable Graph Learning for Anti-Money Laundering: A First Lookcited by 33
  13. 2015Clustering attributed graphs: models, measures and methodscited by 20
  14. 2016Deep Learning in Financecited by 18
  15. 2017Unifying DAGs and UGscited by 2

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 1; 2025: 1.

4 papers.

  1. 2023A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detectioncited by 10
  2. 2025Year-over-Year Developments in Financial Fraud Detection via Deep Learning: A Systematic Literature Reviewcited by 4
  3. 2023From the Pursuit of Universal AGI Architecture to Systematic Approach to Heterogenous AGI: Addressing Alignment, Energy, & AGI Grand Challengescited by 1
  4. 2024Cybercrime Prediction via Geographically Weighted Learningcited by 1