# Can LLMs Find Fraudsters? Multi-level LLM Enhanced Graph Fraud Detection

arXiv: https://arxiv.org/abs/2507.11997 · HTML: https://mlsanity.com/paper/2507.11997/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 44 references that are in the map.
- First posted on arXiv 16 Jul 2025.

## Builds on (most cited first, 22 of 22)

- [Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters](https://mlsanity.com/paper/2008.08692/) (2020)
- [Rethinking Graph Neural Networks for Anomaly Detection](https://mlsanity.com/paper/2205.15508/) (2022)
- [LLMRec: Large Language Models with Graph Augmentation for Recommendation](https://mlsanity.com/paper/2311.00423/) (2023)
- [GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks](https://mlsanity.com/paper/2302.08043/) (2023)
- [Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction](https://mlsanity.com/paper/2111.00064/) (2021)
- [From Amateurs to Connoisseurs: Modeling the Evolution of User Expertise through Online Reviews](https://mlsanity.com/paper/1303.4402/) (2013)
- [Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection](https://mlsanity.com/paper/2005.00625/) (2020)
- [AV-Deepfake1M: A Large-Scale LLM-Driven Audio-Visual Deepfake Dataset](https://mlsanity.com/paper/2311.15308/) (2023)
- [TextGNN: Improving Text Encoder via Graph Neural Network in Sponsored Search](https://mlsanity.com/paper/2101.06323/) (2021)
- [Towards Self-Interpretable Graph-Level Anomaly Detection](https://mlsanity.com/paper/2310.16520/) (2023)
- [Alleviating Structural Distribution Shift in Graph Anomaly Detection](https://mlsanity.com/paper/2401.14155/) (2024)
- [Embracing the Dark Knowledge: Domain Generalization Using Regularized Knowledge Distillation](https://mlsanity.com/paper/2107.02629/) (2021)
- [SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs](https://mlsanity.com/paper/2305.13573/) (2023)
- [Partitioning Message Passing for Graph Fraud Detection](https://mlsanity.com/paper/2412.00020/) (2024)
- [Label Information Enhanced Fraud Detection against Low Homophily in Graphs](https://mlsanity.com/paper/2302.10407/) (2023)
- [Distilling Large Language Models for Text-Attributed Graph Learning](https://mlsanity.com/paper/2402.12022/) (2024)
- [SLADE: Detecting Dynamic Anomalies in Edge Streams without Labels via Self-Supervised Learning](https://mlsanity.com/paper/2402.11933/) (2024)
- [Understanding the Impact of AI Generated Content on Social Media: The Pixiv Case](https://mlsanity.com/paper/2402.18463/) (2024)
- [Multimodal LLM Enhanced Cross-lingual Cross-modal Retrieval](https://mlsanity.com/paper/2409.19961/) (2024)
- [TouchUp-G: Improving Feature Representation through Graph-Centric Finetuning](https://mlsanity.com/paper/2309.13885/) (2023)
- [One-Step Detection Paradigm for Hyperspectral Anomaly Detection via Spectral Deviation Relationship Learning](https://mlsanity.com/paper/2303.12342/) (2023)
- [Heterogeneous Graph Neural Network with Multi-view Representation Learning](https://mlsanity.com/paper/2108.13650/) (2021)

## Built on by (most cited first, 10 of 10)

- [Understanding Structured Financial Data with LLMs: A Case Study on Fraud Detection](https://mlsanity.com/paper/2512.13040/) (2025)
- [SHERLOCK: Towards Dynamic Knowledge Adaptation in LLM-enhanced E-commerce Risk Management](https://mlsanity.com/paper/2510.08948/) (2025)
- [Reinforcement Learning of Large Language Models for Interpretable Credit Card Fraud Detection](https://mlsanity.com/paper/2601.05578/) (2026)
- [Autonomous Chain-of-Thought Distillation for Graph-Based Fraud Detection](https://mlsanity.com/paper/2601.22949/) (2026)
- [Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection](https://mlsanity.com/paper/2605.27470/) (2026)
- [Let Relations Speak: An End-to-End LLM-GNN Soft Prompt Framework for Fraud Detection](https://mlsanity.com/paper/2605.28524/) (2026)
- [L2IR: Revealing Latent Intent in Graph Fraud Detection](https://mlsanity.com/paper/2605.26040/) (2026)
- [UNIT: Unleash Large Language Models Potential for Graph Continual Learning](https://mlsanity.com/paper/2607.10159/) (2026)
- [Operational Evidence Gaps for LLMs in Fraud Detection and Trust-and-Safety Workflows](https://mlsanity.com/paper/2607.13078/) (2026)
- [Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation](https://mlsanity.com/paper/2607.19266/) (2026)

## Abstract (arXiv)

Graph fraud detection has garnered significant attention as Graph Neural Networks (GNNs) have proven effective in modeling complex relationships within multimodal data. However, existing graph fraud detection methods typically use preprocessed node embeddings and predefined graph structures to reveal fraudsters, which ignore the rich semantic cues contained in raw textual information. Although Large Language Models (LLMs) exhibit powerful capabilities in processing textual information, it remains a significant challenge to perform multimodal fusion of processed textual embeddings with graph structures. In this paper, we propose a Multi-level LLM Enhanced Graph Fraud Detection framework called MLED. In MLED, we utilize LLMs to extract external knowledge from textual information to enhance graph fraud detection methods. To integrate LLMs with graph structure information and enhance the ability to distinguish fraudsters, we design a multi-level LLM enhanced framework including type-level enhancer and relation-level enhancer. One is to enhance the difference between the fraudsters and the benign entities, the other is to enhance the importance of the fraudsters in different relations. The experiments on four real-world datasets show that MLED achieves state-of-the-art performance in graph fraud detection as a generalized framework that can be applied to existing methods.