Advanced Financial Fraud Detection Using GNN-CL Model

ArXiv ID: 2407.06529 “View on arXiv”

Authors: Unknown

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.

Keywords: graph neural networks (GNN), financial fraud detection, convolutional neural networks (CNN), long short-term memory (LSTM), reinforcement learning, N/A (Risk/Compliance)

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: 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.

  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