Advanced Financial Fraud Detection Using GNN-CL Model

TL;DR

This paper introduces the GNN-CL model, which combines GNN, CNN, and LSTM to enhance financial fraud detection by filtering noise and reinforcing key features, resulting in improved accuracy and robustness.

Contribution

The paper presents a novel GNN-CL model that integrates multilayer perceptrons and reinforcement learning to address noise filtering and feature weakening in fraud detection.

Findings

GNN-CL outperforms existing methods on Yelp datasets.

Effective noise filtering improves detection accuracy.

Reinforcement learning enhances feature importance retention.

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.