Cross-Regional Fraud Detection via Continual Learning (Student Abstract)
Keywords:Financial Fraud Detection, Forgetting Prevention, Continual Learning, Graph Neural Network
AbstractDetecting fraud is an urgent task to avoid transaction risks. Especially when expanding a business to new cities or new countries, developing a totally new model will bring the cost issue and result in forgetting previous knowledge. This study proposes a novel solution based on heterogeneous trade graphs, namely HTG-CFD, to prevent knowledge forgetting of cross-regional fraud detection. Specifically, a novel heterogeneous trade graph is meticulously constructed from original transactions to explore the complex semantics among different types of entities and relationships. Motivated by continual learning, we present a practical and task-oriented forgetting prevention method to alleviate knowledge forgetting in the context of cross-regional detection. Extensive experiments demonstrate that HTG-CFD promotes performance in both cross-regional and single-regional scenarios.
How to Cite
Li, Y., Yang, Y., Gao, Q., & Yang, X. (2023). Cross-Regional Fraud Detection via Continual Learning (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16260-16261. https://doi.org/10.1609/aaai.v37i13.26990
AAAI Student Abstract and Poster Program