  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.
