Cross-Regional Fraud Detection via Continual Learning (Student Abstract)
Yujie Li, Yuxuan Yang, Qiang Gao, Xin Yang
Abstract
Detecting 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.
BibTeX
@article{Li_Yang_Gao_Yang_2024, title={Cross-Regional Fraud Detection via Continual Learning (Student Abstract)}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/26990}, DOI={10.1609/aaai.v37i13.26990}, abstractNote={Detecting 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.}, number={13}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Li, Yujie and Yang, Yuxuan and Gao, Qiang and Yang, Xin}, year={2024}, month={Jul.}, pages={16260-16261} }