Abstract
Fraud is on the rise under modern e-commence scenarios, which will critically damage the market system. Thus, it is essential to detect fraudsters to prevent unpredictable risks. There are two challenges toward this problem. First, real world fraud detection usually lack of labeled samples. Second, recent ML-based detection method lack of interpretation. Knowledge may help with these problems. Hence, we propose a Knowledge-Guided Graph Neural Network, namely Know-GNN, which utilizes the expertise to roughly mark unlabeled data and uses an explainable semi-supervised method to train a fraud detector. We adopt Graph Functional Dependency (GFD) as a uniform expression of knowledge to mark unlabeled data and give explanations of the detection results. Experiments on banking transaction funds supervision data (BTFSD) demonstrate the effectiveness of our model. By utilizing only 13 GFD rules conducted by domain experts corresponding to BTFSD, the performance of our method yields about 14% improvement over the state-of-the-art methods, CARE-GNN. Moreover, the interpretable results can give interesting intuitions about the fraud detection tasks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Dou, Y., Liu, Z., Sun, L., Deng, Y., Peng, H., Yu, P.S.: Enhancing graph neural network-based fraud detectors against Camouflaged Fraudsters. In: International Conference on Information and Knowledge Management, Proceedings, pp. 315–324 (2020)
Fan, W.: Dependencies for graphs: challenges and opportunities. J. Data Inf. Qual. 11(2), 1–10 (2019)
Fan, W., Hu, C., Liu, X., Lu, P.: Discovering graph functional dependencies. ACM Trans. Database Syst. 45(3), 1–42 (2020)
Fan, W., Jin, R., Liu, M., Lu, P., Tian, C., Zhou, J.: Capturing associations in graphs. Proc. VLDB Endow. 13(11), 1863–1876 (2020)
Fan, W., Lu, P.: Dependencies for graphs. ACM Trans. Database Syst. 44(2), 1–56 (2019)
Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016)
Han, B., et al.: Co-teaching: Robust training of deep neural networks with extremely noisy labels. arXiv (NeurIPS), pp. 1–11 (2018)
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. Computer Science (2014)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (ICLR) (2017)
Sadowksi, G., Rathle, P.: Fraud detection: Discovering connections with graph databases. [EB/OL] (2014)
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. In: International Conference on Learning Representations (2018)
Zheng, W., Yan, L., Gou, C., Wang, F.Y.: Federated meta-learning for fraudulent credit card detection. In: IJCAI International Joint Conference on Artificial Intelligence 2021, pp. 4654–4660 (2020)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Rao, Y. et al. (2021). Know-GNN: An Explainable Knowledge-Guided Graph Neural Network for Fraud Detection. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Communications in Computer and Information Science, vol 1516. Springer, Cham. https://doi.org/10.1007/978-3-030-92307-5_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-92307-5_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-92306-8
Online ISBN: 978-3-030-92307-5
eBook Packages: Computer ScienceComputer Science (R0)