Paper: arXiv 2404.00060

Abstract

This paper explores the utilization of Temporal Graph Networks (TGN) for financial anomaly detection, a pressing need in the era of fintech and digitized financial transactions. We present a comprehensive framework that leverages TGN, capable of capturing dynamic changes in edges within financial networks, for fraud detection. Our study compares TGN’s performance against static Graph Neural Network (GNN) baselines, as well as cutting-edge hypergraph neural network baselines using DGraph dataset for a realistic financial context. Our results demonstrate that TGN significantly outperforms other models in terms of AUC metrics. This superior performance underlines TGN’s potential as an effective tool for detecting financial fraud, showcasing its ability to adapt to the dynamic and complex nature of modern financial systems. We also experimented with various graph embedding modules within the TGN framework and compared the effectiveness of each module. In conclusion, we demonstrated that, even with variations within TGN, it is possible to achieve good performance in the anomaly detection task.

Complexity vs Empirical Score

  • Math Complexity: 7.0/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced temporal graph neural network mathematics with custom embedding functions and attention mechanisms, while demonstrating strong empirical rigor through backtesting on a large-scale real-world financial dataset (DGraph) and comparing multiple baselines with AUC metrics.

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Analyze TGN for Financial Anomaly Detection"] --> B["Dataset & Input<br>DGraph Financial Dataset<br>Dynamic Edges"]
  B --> C["Methodology: Model Comparison<br>TGN vs. Static GNNs<br>vs. Hypergraph NNs"]
  C --> D["Computational Process<br>Temporal Training<br>Embedding Module Analysis"]
  D --> E["Key Findings & Outcomes<br>TGN significantly outperforms baselines<br>in AUC metrics<br>Proves adaptability to dynamic<br>financial systems"]

Cited References (21)

  1. Intelligent financial fraud detection: a comprehensive review (2016)
  2. The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature (2011)
  3. Temporal Graph Networks for Deep Learning on Dynamic Graphs (2020) — link
  4. Saint: Improved neural networks for tabular data via row attention and contrastive pre-training (2021) — link
  5. Dgraph: A large-scale financial dataset for graph anomaly detection (2022)
  6. Multilayer perceptron neural network technique for fraud detection (2017)
  7. Semi-supervised classification with graph convolutional networks (2016) — link
  8. Inductive representation learning on large graphs (2017)
and 13 more
  1. Inductive representation learning on temporal graphs (2020) — link
  2. Graph attention networks (2017) — link
  3. Hnhn: Hypergraph networks with hyperedge neurons (2020) — link
  4. The adoption of mobile payment services for “Fintech” (2016)
  5. The real impact of FinTech: Evidence from mobile payment technology (2020)
  6. Mobile payment in Fintech environment: trends, security challenges, and services (2018)
  7. Hypergraph neural networks (2019)
  8. Hypergraph convolution and hypergraph attention (2021)
  9. You are allset: A multiset function framework for hypergraph neural networks (2021) — link
  10. Set transformer: A framework for attention-based permutation-invariant neural networks (2019)
  11. Empirical evaluation of gated recurrent neural networks on sequence modeling (2014) — link
  12. Graph based anomaly detection and description: a survey (2015)
  13. Realistic Synthetic Financial Transactions for Anti-Money Laundering Models — → 2306.16424

1 of 21 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.