Explainable AI in Financial Fraud Detection: Evidence from the IEEE-CIS Fraud Dataset
By: Delia DIACONU and Daniel Traian PELE
References
- Akoglu, L., Tong, H., & Koutra, D. (2015). Graph-based anomaly detection and description: A survey. Data Mining and Knowledge Discovery, Springer, V.29, pp. 626–688.
- Bahnsen, A. C., Aouada, D., & Ottersten, B. (2015). Example-dependent cost-sensitive decision trees. Expert Systems with Applications, Vol. 42, Issue 19, pp. 6609–6619.
- Dou, Y., Liu, Z., Sun, L., Deng, Y., Peng, H., & Yu, P. S. (2020). Enhancing graph neural network-based fraud detectors against camouflaged fraudsters. ACM CIKM Conference on Information and Knowledge Management, pp. 315–324.
- Hamilton, W. L., Ying, Z., & Leskovec, J. (2017). Inductive representation learning on large graphs. Neural Information Processing Systems.
- Li, Y., Chen, X., Zhang, J., et al. (2025). Graph neural networks for financial fraud detection: a review. Frontiers of Computer Science, Vol. 19(9).
- Li, Z., Wang, Y., Zhang, Q., et al. (2023). Label Information Enhanced Fraud detection against low homophily in graphs. Proceedings of the ACM Web Conference, pp. 406–416.
- Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems (NeurIPS), pp. 4766–4777.
- Luo, D., Cheng, W., Xu, D., Yu, W., Zong, B., Chen, H., & Zhang, X. (2020). PGExplainer: Parameterized Explainer for Graph Neural Network. Neural Information Processing Systems, pp. 19620–19631.
- Pandit, S., Chau, D. H., Wang, S., & Faloutsos, C. (2007). NetProbe: A fast and scalable system for fraud detection in online auction networks. Proceedings of the 16th international conference on WWW, pp. 201–210.
- Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should I trust you?”: Explaining the predictions of any classifier. ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 1135–1144.
- Schlichtkrull, M., De Cao, N., & Titov, I. (2021). Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking. ICLR Conference.
- Ying, R., Bourgeois, D., You, J., Zitnik, M., & Leskovec, J. (2019). GNNExplainer: Generating Explanations for Graph Neural Networks. Neural Information Processing Systems.
- Yuan, H., Tang, J., Hu, X., & Ji, S. (2021). XGNN: Towards Model-Level Explanations of Graph Neural Networks. Proceedings of the 40th International Conference on Machine Learning, pp. 33674–33719.
- Yuan, H., Yu, H., Gui, S., Ji, S. (2020). Explainability in graph neural networks: a taxonomic survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol, 45(5), pp. 5782–5799.
DOI: https://doi.org/10.2478/picbe-2026-0015 | Journal eISSN: 2558-9652
Language: English
Page range: 118 - 132
Published on: Jul 15, 2026
Published by: Bucharest University of Economic Studies
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
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© 2026 Delia DIACONU, Daniel Traian PELE, published by Bucharest University of Economic Studies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.