A Graph Neural Network-Based Approach for Detecting Fraudulent Small-Value High-Frequency Accounting Transactions
DOI:
https://doi.org/10.5281/zenodo.14161459ARK:
https://n2t.net/ark:/40704/AJSM.v2n6a05Disciplines:
FinanceSubjects:
Financial Risk ManagementReferences:
35Keywords:
Financial Risk Management, Graph Neural Networks, Fraud Detection, Small-Value Transactions, Temporal-Spatial PatternsAbstract
The growth of digital accounting systems has led to increased fraud schemes, especially those involving small-value businesses. This paper presents a novel neural network architecture to capture order to break it down to break up suitable class and image heterophily in fraud detection by holding different representations for homophilic and heterophilic characteristics, making it more effective in detecting fraud patterns. The model includes a unique system of body-aware construction and adaptive memory to capture complex changes on multiple time scales. We introduce a two-channel feature extraction mechanism that performs similar and different processes independently, facilitating the storage and propagation of fraud signals from the business network. Various experiments on two real-world datasets show that our method significantly improved over the state-of-the-art method, with a performance of 12.3% in AUC -ROC and 15.7% in F1-score. The model is particularly effective in identifying fraud schemes that use multiple accounts and different currencies, achieving a 67% reduction in false positives. Our results show the model can identify subtle transaction patterns that distinguish fraudulent from legitimate transactions.
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