Advanced Financial Fraud Detection Using GNN-CL Model

Paper: arXiv 2407.06529

Abstract

The innovative GNN-CL model proposed in this paper marks a breakthrough in the field of financial fraud detection by synergistically combining the advantages of graph neural networks (gnn), convolutional neural networks (cnn) and long short-term memory (LSTM) networks. This convergence enables multifaceted analysis of complex transaction patterns, improving detection accuracy and resilience against complex fraudulent activities. A key novelty of this paper is the use of multilayer perceptrons (MLPS) to estimate node similarity, effectively filtering out neighborhood noise that can lead to false positives. This intelligent purification mechanism ensures that only the most relevant information is considered, thereby improving the model’s understanding of the network structure. Feature weakening often plagues graph-based models due to the dilution of key signals. In order to further address the challenge of feature weakening, GNN-CL adopts reinforcement learning strategies. By dynamically adjusting the weights assigned to central nodes, it reinforces the importance of these influential entities to retain important clues of fraud even in less informative data. Experimental evaluations on Yelp datasets show that the results highlight the superior performance of GNN-CL compared to existing methods.

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper involves advanced neural network architectures (GNN, CNN, LSTM, MLP) and a reinforcement learning component, constituting high math complexity; it also includes experimental evaluations on a specific dataset (Yelp) with comparative performance metrics, indicating substantial empirical rigor.

Research Flowchart

  flowchart TD
  subgraph A ["Research Goal"]
      G["Improve Financial Fraud Detection"]
  end

  subgraph B ["Data & Inputs"]
      D["Yelp Dataset"]
  end

  subgraph C ["Methodology: GNN-CL Model"]
      C1["Feature Extraction<br>CNN + LSTM"]
      C2["Graph Processing<br>GNN"]
      C3["Noise Filtering<br>MLP for Node Similarity"]
      C4["Dynamic Weighting<br>Reinforcement Learning"]
  end

  subgraph E ["Outcomes"]
      F["Superior Detection Accuracy"]
      H["Resilience to Fraudulent Activities"]
      I["Reduced False Positives"]
  end

  A --> B
  B --> C
  C1 --> C2
  C2 --> C3
  C3 --> C4
  C4 --> E

Cited References (32)

1. A new method of image recognition based on deep learning generates adversarial networks and integrates traditional algorithms (2024) — link
2. An Enhanced Encoder-Decoder Network Architecture for Reducing Information Loss in Image Semantic Segmentation (2024) — link
3. Investigation of Customized Medical Decision Algorithms Utilizing Graph Neural Networks (2024) — link
4. Integrating Medical Imaging and Clinical Reports Using Multimodal Deep Learning for Advanced Disease Analysis (2024) — link
5. Exploration of Attention Mechanism-Enhanced Deep Learning Models in the Mining of Medical Textual Data (2024) — link
6. Efficiency Optimization of Large-Scale Language Models Based on Deep Learning in Natural Language Processing Tasks (2024) — link
7. Innovations in Time Related Expression Recognition Using LSTM Networks (2024) — link
8. Deep Learning-Based Lung Medical Image Recognition (2024) — link

0 of 32 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.