Explain First, Trust Later: LLM-Augmented Explanations for Graph-Based Crypto Anomaly Detection
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Abstract (arXiv)
The decentralized finance (DeFi) community has grown rapidly in recent years, pushed forward by cryptocurrency enthusiasts interested in the vast untapped potential of new markets. The surge in popularity of cryptocurrency has ushered in a new era of financial crime. Unfortunately, the novelty of the technology makes the task of catching and prosecuting offenders particularly challenging. Thus, it is necessary to implement automated detection tools related to policies to address the growing criminality in the cryptocurrency realm.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 10 of its 56 references that are in the map. First posted on arXiv 17 Jun 2025.
Builds on
10 of 56 references.
References · 10
- 2020GraphLIME: Local Interpretable Model Explanations for Graph Neural Networkscited by 209
- 2019Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensicscited by 164
- 2024Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Eracited by 46
- 2024XAI meets LLMs: A Survey of the Relation between Explainable AI and Large Language Modelscited by 23
- 2023Demystifying Fraudulent Transactions and Illicit Nodes in the Bitcoin Network for Financial Forensicscited by 19
- 2024LLMs for XAI: Future Directions for Explaining Explanationscited by 19
- 2021GuiltyWalker: Distance to illicit nodes in the Bitcoin networkcited by 13
- 2024GraphXAIN: Narratives to Explain Graph Neural Networkscited by 11
- 2024Detecting Anomalies in Blockchain Transactions using Machine Learning Classifiers and Explainability Analysiscited by 4
- 2023Enhancing Illicit Activity Detection using XAI: A Multimodal Graph-LLM Frameworkcited by 1
Built on by
Citing papers per year: 2025: 1; 2026: 1.
2 papers.