{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T03:41:39Z","timestamp":1760326899956,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032083296","type":"print"},{"value":"9783032083302","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:00:00Z","timestamp":1760400000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:00:00Z","timestamp":1760400000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Fraudulent activity in the financial industry costs billions annually. Detecting fraud, therefore, is an essential yet technically challenging task that requires carefully analyzing large volumes of data. While machine learning (ML) approaches seem like a viable solution, applying them successfully is not so easy due to two main challenges: (1) the sparsely labeled data, which makes the training of such approaches challenging (with inherent labeling costs), and (2) lack of explainability for the flagged items posed by the opacity of ML models, that is often required by business regulations. This article proposes SAGE-FIN, a semi-supervised graph neural network (GNN) based approach with Granger causal explanations for Financial Interaction Networks. SAGE-FIN learns to flag fraudulent items based on weakly labeled (or unlabelled) data points. To adhere to regulatory requirements, the flagged items are explained by highlighting related items in the network using Granger causality. We empirically validate the favorable performance of SAGE-FIN on a real-world dataset, Bipartite Edge-And-Node Attributed financial network (Elliptic++), with Granger-causal explanations for the identified fraudulent items without any prior assumption on the network structure.<\/jats:p>","DOI":"10.1007\/978-3-032-08330-2_16","type":"book-chapter","created":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T03:11:18Z","timestamp":1760325078000},"page":"330-353","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Detecting Fraud in\u00a0Financial Networks: A Semi-supervised GNN Approach with\u00a0Granger-Causal Explanations"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-2797-5598","authenticated-orcid":false,"given":"Linh","family":"Nguyen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9656-7249","authenticated-orcid":false,"given":"Marcel","family":"Boersma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9038-6020","authenticated-orcid":false,"given":"Erman","family":"Acar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,14]]},"reference":[{"key":"16_CR1","doi-asserted-by":"crossref","unstructured":"Albashrawi, M.: Detecting financial fraud using data mining techniques: a decade review from 2004 to 2015. J. Data Sci. 14, 553\u2013570 (2016)","DOI":"10.6339\/JDS.201607_14(3).0010"},{"key":"16_CR2","doi-asserted-by":"crossref","unstructured":"Ngai, T., Hu, Y., Wong, H., Chen, Y., Sun, X.:The application of data mining techniques in financial fraud detection: a classification framework and an academic review of literature. J. Decis. Support Syst. 50, 559\u2013569 (2011)","DOI":"10.1016\/j.dss.2010.08.006"},{"key":"16_CR3","doi-asserted-by":"crossref","unstructured":"Chandola, V.,Banerjee, A., Kumar, V.:Anomaly detection: a survey. ACM Comput. Surv. (CSUR) 41(3), 1\u201358 (2009)","DOI":"10.1145\/1541880.1541882"},{"key":"16_CR4","unstructured":"Thilakarathna, K., Fukuda, K., Seneviratne, A., Hu, Y., Seneviratne, S.: Characterizing and detecting money laundering activities on the bitcoin network. preprint arXiv:1912.12060 (2019)"},{"key":"16_CR5","doi-asserted-by":"crossref","unstructured":"Monamo, P., Marivate, V., Twala, B.: A multifaceted approach to Bitcoin fraud detection: global and local outliers. In: 15th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 188\u2013194. IEEE (2016)","DOI":"10.1109\/ICMLA.2016.0039"},{"key":"16_CR6","doi-asserted-by":"publisher","unstructured":"Visbeek, S., Acar, E., Hengst, F.: Explainable fraud detection with deep symbolic classification. In: World Conference on Explainable Artificial Intelligence, pp. 350\u2013373. Springer (2024). https:\/\/doi.org\/10.1007\/978-3-031-63800-8_18","DOI":"10.1007\/978-3-031-63800-8_18"},{"key":"16_CR7","unstructured":"Azarm, C, Zeelt, M., Acar, E.: On the potential of network-based features for fraud detection (2024)"},{"key":"16_CR8","unstructured":"Boersma, M.: Complex networks in audit: a data-driven modelling approach. Universiteit van Amsterdam (2024)"},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"Sourabh, S., Hoogduin, L., Kandhai, D., Boersma, M.: Financial statement networks: an application of network theory in audit. J. Netw. Theory Finan. 4, 59\u201385 (2018)","DOI":"10.21314\/JNTF.2018.048"},{"key":"16_CR10","doi-asserted-by":"publisher","first-page":"26","DOI":"10.3389\/frai.2020.00026","volume":"3","author":"N Bussmann","year":"2020","unstructured":"Bussmann, N., Giudici, P., Marinelli, D., Papenbrock, J.: Explainable AI in fintech risk management. Front. Artif. Intell. 3, 26 (2020)","journal-title":"Front. Artif. Intell."},{"key":"16_CR11","doi-asserted-by":"crossref","unstructured":"Gomber, P., Kauffman, RJ., Parker, C., Weber, B.W.: On the fintech revolution: interpreting the forces of innovation, disruption, and transformation in financial services. J. Manag. Inf. Syst. 35(1), 220\u2013265 (2018)","DOI":"10.1080\/07421222.2018.1440766"},{"key":"16_CR12","doi-asserted-by":"crossref","unstructured":"Weber, P., Carl, KV., Hinz, O.: Applications of explainable artificial intelligence in finance\u2014a systematic review of finance, information systems, and computer science literature. Manag. Rev. Q. 74(2), 867\u2013907 (2024)","DOI":"10.1007\/s11301-023-00320-0"},{"key":"16_CR13","first-page":"316","volume":"13","author":"S Yablo","year":"2003","unstructured":"Yablo, S.: Causal relevance. Philos. Issues 13, 316\u2013328 (2003)","journal-title":"Issues"},{"key":"16_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122156","volume":"240","author":"S Motie","year":"2024","unstructured":"Motie, S., Raahemi, B.: Financial fraud detection using graph neural networks: a systematic review. J. Expert Syst. Appl. 240, 122156 (2024)","journal-title":"J. Expert Syst. Appl."},{"key":"16_CR15","doi-asserted-by":"crossref","unstructured":"Elmougy, Y., Liu, L.: Demystifying fraudulent transactions and illicit nodes in the bitcoin network for financial forensics. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2023)","DOI":"10.1145\/3580305.3599803"},{"key":"16_CR16","unstructured":"Weber, M., et al: Anti-money laundering in bitcoin: experimenting with graph convolutional networks for financial forensics. ArXiv abs\/1908.02591 (2019)"},{"key":"16_CR17","unstructured":"Kipf, TN., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv (2016)"},{"key":"16_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40649-019-0069-y","volume":"6","author":"S Zhang","year":"2019","unstructured":"Zhang, S., Tong, H., Xu, J., Maciejewski, R.: Graph convolutional networks: a comprehensive review. Comput. Soc. Netw. 6, 1 (2019)","journal-title":"Comput. Soc. Netw."},{"key":"16_CR19","unstructured":"Zhou, J., et al.: Graph neural networks: a review of methods and applications. arXiv (2018)"},{"key":"16_CR20","doi-asserted-by":"crossref","unstructured":"Ghosh, S., Anand, R., Bhowmik, T., Chandrashekhar, S.: GoSage: heterogeneous graph neural network using hierarchical attention for collusion fraud detection. In 4th ACM International Conference on AI in Finance. ACM, New York, NY, USA (2023)","DOI":"10.1145\/3604237.3626856"},{"key":"16_CR21","doi-asserted-by":"crossref","unstructured":"Jiang, S., Uddin, A., Wei, Z, Yu, D.: The network of mutual funds: a dynamic heterogeneous graph neural network for estimating mutual funds performance. In 4th ACM International Conference on AI in Finance. ACM, New York, NY, USA (2023)","DOI":"10.1145\/3604237.3626910"},{"key":"16_CR22","doi-asserted-by":"crossref","unstructured":"Cao, Z., Chen, Z., Mishra, P, Amini, H., Feinstein, Z.: Modeling inverse demand function with explainable dual neural networks. In 4th ACM International Conference on AI in Finance. ACM, New York, NY, USA (2023)","DOI":"10.2139\/ssrn.4521967"},{"key":"16_CR23","doi-asserted-by":"crossref","unstructured":"Giudici, P., Spelta,A.: Graphical network models for international financial flows. J. Bus. Econ. Stat. 34, 128\u2013138 (2016)","DOI":"10.1080\/07350015.2015.1017643"},{"key":"16_CR24","doi-asserted-by":"crossref","unstructured":"Billio, M., Getmansky, M., Lo, A., Pelizzon, L.: Econometric measures of connectedness and systemic risk in the finance and insurance sectors. J. Finan. Econ. 104(3), 535\u2013559 (2012)","DOI":"10.1016\/j.jfineco.2011.12.010"},{"key":"16_CR25","doi-asserted-by":"crossref","unstructured":"Battiston, S., Puliga, M, Kaushik, R., Tasca, P., Caldarelli, G.: DebtRank: too central to fail? Financial networks, the FED and systemic risk. Nat. Sci. Rep. 2, 541 (2012)","DOI":"10.1038\/srep00541"},{"key":"16_CR26","doi-asserted-by":"publisher","unstructured":"Akoglu, L., Tong, H., Koutra, D.: Graph based anomaly detection and description: a survey, DMKD J. (2015). https:\/\/doi.org\/10.1007\/s10618-014-0365-y","DOI":"10.1007\/s10618-014-0365-y"},{"key":"16_CR27","doi-asserted-by":"crossref","unstructured":"Jing, R., et al.: A graph-based semi-supervised fraud detection framework. In: 4th IEEE International Conference on Cybernetics (Cybconf), pp. 1\u20135 (2019)","DOI":"10.1109\/Cybconf47073.2019.9436573"},{"key":"16_CR28","doi-asserted-by":"crossref","unstructured":"Rao, Y., et al.: Knowledge guided fraud detection using semi-supervised graph neural network. In: Web Information Systems Engineering\u2013WISE 2021: 22nd International Conference, pp. 385\u2013393 (2021)","DOI":"10.1007\/978-3-030-90888-1_29"},{"key":"16_CR29","doi-asserted-by":"crossref","unstructured":"Fathony, R., Ng, J., Chen, J.: Interaction-focused anomaly detection on bipartite node-and-edge-attributed graphs. In: International Joint Conference on Neural Networks (IJCNN) (2023)","DOI":"10.1109\/IJCNN54540.2023.10191331"},{"key":"16_CR30","unstructured":"Yuan, H., Yu, H., Gui, S., Ji, S.: Explainability in graph neural networks: a taxonomic survey. IEEE Trans. Pattern Anal. Mach. Intell. 45(5), 5782\u20135799 (2023)"},{"key":"16_CR31","doi-asserted-by":"crossref","unstructured":"Yuan, H., Tang, J., Hu, X., Ji, S.: XGNN: towards model-level explanations of graph neural networks. In: Proceedings SIGKDD ACM (2020)","DOI":"10.1145\/3394486.3403085"},{"key":"16_CR32","doi-asserted-by":"crossref","unstructured":"Giudici, P.: Learning in Graphical Gaussian Models, Bayesian Statistics, vol. 5, pp. 621\u2013628 (1996)","DOI":"10.1093\/oso\/9780198523567.003.0040"},{"key":"16_CR33","doi-asserted-by":"crossref","unstructured":"Ahelegbey, A., Giudici, P.: NetVIX - a network volatility index for financial markets. Physica A: Stat. Mech. Appl. 594, 127017 (2022)","DOI":"10.1016\/j.physa.2022.127017"},{"key":"16_CR34","unstructured":"Ying, Z., Bourgeois, D., You, J., Zitnik, M., Leskovec, J.: Generating explanations for graph neural networks. In: Proceedings of Advances in Neural Information Processing Systems Conference, pp. 9244\u20139255 (2019)"},{"key":"16_CR35","unstructured":"Vu, N. Thai, T: Probabilistic graphical model explanations for graph neural networks. In: Proceedings of Advances in Neural Information Processing Systems Conference (2020)"},{"key":"16_CR36","unstructured":"Li, B., Lin, W., Lan, H.: Generative causal explanations for graph neural network (2021)"},{"key":"16_CR37","doi-asserted-by":"crossref","unstructured":"Granger, C.: Investigating causal relations by econometric models and cross-spectral methods. Econometrica: J. Econ. Soc. 37, 424\u2013438 (1969)","DOI":"10.2307\/1912791"},{"key":"16_CR38","doi-asserted-by":"crossref","unstructured":"Tank, A., Covert, I., Foti, N., Shojaie, A., Fox, E.: Neural Granger causality. IEEE Trans. Pattern Anal. Mach. Intell. 44(8), 4267\u20134279 (2021)","DOI":"10.1109\/TPAMI.2021.3065601"},{"key":"16_CR39","doi-asserted-by":"crossref","unstructured":"Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., Elhadad, N.: Intelligible models for healthcare: predicting pneumonia risk and hospital 30-day readmission. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1721\u20131730 (2015)","DOI":"10.1145\/2783258.2788613"},{"key":"16_CR40","unstructured":"Lundberg, S. M., Lee, S.I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems (NeurIPS), pp. 4765\u20134774 (2017)"},{"key":"16_CR41","unstructured":"Pearl, J., Glymour, M., Jewell, NP.: Causal Inference in Statistics: A primer. Wiley (2016)"},{"key":"16_CR42","doi-asserted-by":"crossref","unstructured":"Babaei, G., Giudici, P., Raffinetti, E.: Explainable artificial intelligence for crypto asset allocation. Finance Res. Lett. 47, 102941 (2022)","DOI":"10.1016\/j.frl.2022.102941"}],"container-title":["Communications in Computer and Information Science","Explainable Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-08330-2_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T03:11:31Z","timestamp":1760325091000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-08330-2_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,14]]},"ISBN":["9783032083296","9783032083302"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-08330-2_16","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,14]]},"assertion":[{"value":"14 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"xAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"World Conference on Explainable Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Istanbul","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"T\u00fcrkiye","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"xai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/xaiworldconference.com\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}