Publication
Abstract
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.
Keywords
Financial Risk Management , Graph Neural Networks , Fraud Detection , Small-Value Transactions , Temporal-Spatial Patterns .
Metadata
Subjects: Financial Risk Management
Cite This Article
APA Style
Zhang, Y., Liu, Y. & Zheng, S. (2024). A graph neural network-based approach for detecting fraudulent small-value high-frequency accounting transactions. Academic Journal of Sociology and Management, 2(6), 25-34. https://doi.org/10.5281/zenodo.14161459
Acknowledgments
I would like to extend my sincere gratitude to Yida Zhu, Siwei Xia, Ming Wei, Yanli Pu, and Zeyu Wang for their groundbreaking research on AI-enhanced administrative prosecutorial supervision in financial big data, as published in their article[34]. Their innovative perspectives on integrating artificial intelligence with financial supervision have significantly influenced my understanding of advanced techniques in fraud detection and have provided valuable inspiration for my research in this critical area.
I would also like to express my heartfelt appreciation to Shikai Wang, Qi Lou, and Yida Zhu for their innovative study on utilizing artificial intelligence for financial risk monitoring in asset management, as published in their article[35]. Their comprehensive analysis and risk monitoring approaches have significantly enhanced my knowledge of financial surveillance systems and inspired my research in this field.
FUNDING
INSTITUTIONAL REVIEW BOARD STATEMENT
DATA AVAILABILITY STATEMENT
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
INFORMED CONSENT STATEMENT
CONFLICT OF INTEREST
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
AUTHOR CONTRIBUTIONS
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