图神经网络能更好识别金融欺诈中的复杂关系模式。
Graph Neural Networks for Financial Fraud Detection: A Review
- 构建统一框架，系统分类金融反欺诈中应用的图神经网络方法。
- 分析超100篇研究，证明GNN在捕捉金融关系模式上显著优于传统方法。
- 适合关注金融安全、图神经网络应用的研究者与从业者参考。
全球经济一体化和信息技术的发展使金融交易环境日益复杂，给金融欺诈检测与管理带来更大挑战。本文综述图神经网络（GNN）在应对这些挑战中的作用，提出一个统一框架，对现有应用于金融欺诈检测的GNN方法进行系统分类。通过深入探讨一系列具体研究问题，本文分析了GNN在金融欺诈检测中的适用性、实际部署情况以及提升其效果的设计考量。研究表明，GNN在捕捉金融网络中的复杂关系模式与动态特征方面表现优异，显著优于传统方法。与以往仅浅层讨论GNN潜力的综述不同，本文提供全面且结构化的分析，聚焦GNN在金融欺诈检测中的多维度应用与部署实践。通过对超过100项研究的系统梳理，本文不仅揭示了GNN在提升欺诈检测能力方面的潜力，也指出现有空白，并提出未来研究方向以推动其在金融系统中的落地应用。
原文摘要 · Abstract (English)
The landscape of financial transactions has grown increasingly complex due to the expansion of global economic integration and advancements in information technology. This complexity poses greater challenges in detecting and managing financial fraud. This review explores the role of Graph Neural Networks (GNNs) in addressing these challenges by proposing a unified framework that categorizes existing GNN methodologies applied to financial fraud detection. Specifically, by examining a series of detailed research questions, this review delves into the suitability of GNNs for financial fraud detection, their deployment in real-world scenarios, and the design considerations that enhance their effectiveness. This review reveals that GNNs are exceptionally adept at capturing complex relational patterns and dynamics within financial networks, significantly outperforming traditional fraud detection methods. Unlike previous surveys that often overlook the specific potentials of GNNs or address them only superficially, our review provides a comprehensive, structured analysis, distinctly focusing on the multifaceted applications and deployments of GNNs in financial fraud detection. This review not only highlights the potential of GNNs to improve fraud detection mechanisms but also identifies current gaps and outlines future research directions to enhance their deployment in financial systems. Through a structured review of over 100 studies, this review paper contributes to the understanding of GNN applications in financial fraud detection, offering insights into their adaptability and potential integration strategies.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。