Detección de fraude digital mediante el uso de grafos
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sep 30, 2025
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https://doi.org/10.30878/ces.v33n0a49
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Diego Saldaña Ulloa
http://orcid.org/0009-0004-1428-6625
Guillermo De Ita Luna
http://orcid.org/0000-0001-7948-8253
http://orcid.org/0009-0004-1428-6625
Guillermo De Ita Luna
http://orcid.org/0000-0001-7948-8253
Resumen
El fraude transaccional en plataformas digitales ha incrementado en años recientes. Los defraudadores utilizan diferentes técnicas para apropiarse de los recursos económicos de usuarios y sacar provecho de algunas vulnerabilidades. Por esta razón, se han implementado diferentes soluciones basadas en el aprendizaje automático para abordar el problema. En este trabajo se describen algunas características del fraude digital así como los principales métodos que se han utilizado para la detección de fraude. A su vez, la transición que ha existido hacia un enfoque que combina grafos con aprendizaje automático, ha propuesto las llamadas redes neuronales de grafos. Finalmente, se mencionan algunos retos del área relacionados a las características de los datos necesarios para operar este tipo de algoritmos.
Article Details
Como citar
SALDAÑA ULLOA, Diego; DE ITA LUNA, Guillermo.
Detección de fraude digital mediante el uso de grafos.
CIENCIA ergo-sum, [S.l.], v. 33, sep. 2025.
ISSN 2395-8782.
Disponible en: <https://cienciaergosum.uaemex.mx/article/view/23217>. Fecha de acceso: 13 feb. 2026
doi: https://doi.org/10.30878/ces.v33n0a49.
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Ciencias exactas y aplicadas
Esta obra está bajo licencia internacional Creative Commons Reconocimiento-NoComercial-SinObrasDerivadas 4.0.
Citas
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CC BY NC ND
Dou, Y., & Liu, Z., & Sun, L., & Deng, Y., & Peng, H., & Yu, P. S. (2020). Enhancing graph neural network-based fraud detectors against camouflaged fraudsters. CoRR, abs/2008.08692. Retrieved from https://arxiv.org/abs/2008.08692
FATF-Interpol - Egmont Group (2023), Illicit Financial Flows from Cyber-Enabled Fraud, FATF, Paris, France, www.fatf-gafi.org/content/fatf-gafi/en/publications/Methodsandtrends/illicit-financial-flows-cyberenabled-fraud.html
Fu, K., & Cheng, D., & Tu, Y., & Zhang, L. (2016). Credit card fraud detection using convolutional neural networks. In Neural information processing (pp. 483–490). Cham:Springer International Publishing.
Hamilton, W. L., & Ying, R., & Leskovec, J. (2017). Inductive representation learning on large graphs. In Proceedings of the 31st international conference on neural information processing systems (pp. 1025–1035). Long Beach, California, USA: Curran Associates Inc.
Jendruszak, B. (2024, 26 de marzo). Credit Card Fraud Detection: The Complete Guide. Seon. https://seon.io/resources/credit-card-fraud-detection
J.P. Morgan. (2021). A Merchant’s Guide to Preventing Card Testing Attacks. https://www.jpmorgan.com/content/dam/jpm/securities/documents/card-testing-white-paper-ada-final.pdf
Lebichot, B., & Braun, F., & Caelen, O., & Saerens, M. (2017). A graph-based, semi-supervised, credit card fraud detection system. In H. Cherifi, S. Gaito, W.
Liu, Z., & Chen, C., & Yang, X., & Zhou, J., & Li, X., & Song, L. (2020). Heterogeneous graph neural networks for malicious account detection. CoRR, abs/2002.12307. arXiv: 2002. 12307. Retrieved from https://arxiv.org/abs/2002.12307
Maess, S., & Tuyls, K., & Vanschoenwinkel, B., & Manderick, B. (2002). Credit card fraud detection using bayesian and neural networks. Proceedings of the first international naiso congress on neuro fuzzy technologies, 2002, Havana, Cuba.
Malone, C. (2023). Online payment fraud: Market forecasts, emerging threats and segment analysis 2023-2028. Accessed: 2023-06-08 12:00 CST. Retrieved from https://www.juniperresearch.com/researchstore/fintechpayments/online%5C%5C-payment-fraud- research-report
Meng, C., & Zhou, L., & Liu, B. (2020). A case study in credit fraud detection with smote and xgboost. Journal of Physics: Conference Series, 1601 (5), 052016. doi:10.1088/1742-6596/1601/5/052016
Quattrociocchi, & A. Sala (Eds.), Complex networks & their applications v (pp. 721–733). Springer International Publishing. Rossi, E., Chamberlain, B., Frasca, F., Eynard, D., Monti, F., & Bronstein, M. (2020). Temporal graph networks for deep learning on dynamic graphs. In Icml 2020 workshop on graph representation learning.
Rao, S. X., & Lanfranchi, C., & Zhang, S., & Han, Z., & Zhang, Z., & Min, W., & .Zhang, C. (2022). Modelling graph dynamics in fraud detection with ”attention”. ArXiv, abs/2204.10614. Retrieved from https://api.semanticscholar.org/CorpusID:248366554
Ravelin. (2024, April 1) Machine learning for fraud detection. https://www.ravelin.com/insights/machine-learning-for-fraud-detection/
Saporta, G., & Maraney, S. (2022). Fraud prevention evaluation and investment in practical fraud prevention (1st ed.). OReilly.
Saldaña-Ulloa, D., & De Ita Luna, G., & Marcial-Romero, J. R. (2024). A Temporal Graph Network Algorithm for Detecting Fraudulent Transactions on Online Payment Platforms. Algorithms, 17(12), 552. https://doi.org/10.3390/a17120552
Seon. (2024) Bank Account Takeover Attacks: How to Keep Safe. https://seon.io/resources/bank-account-takeover-attacks/
Stripe. (2024, April 1) Fraudulent transactions 101: What causes them and how businesses can act. https://stripe.com/in/resources/more/fraudulent-transactions-101
Sulaiman, R., & Schetinin, V., & Sant, P. (2022). Review of machine learning approach on credit card fraud detection. Human-Centric Intelligent Systems, 2 (1–2), 55–68.
Xiao, W., & Houye J., & Chuan S., & Bai W., & Yanfang Y., & Peng C., & Philip S Y. 2019. Heterogeneous Graph Attention Network. In The World Wide Web Conference (WWW '19). Association for Computing Machinery, New York, NY, USA, 2022–2032. https://doi.org/10.1145/3308558.3313562
Yue, D., & Wu, X., & Wang, Y., & Li, Y., & Chu, C.-H. (2007). A review of data mining-based financial fraud detection research. 2007 International Conference on Wireless Communications, Networking and Mobile Computing.
Zhao, P., & Fu, X., & Wu, W., & Li, D., & Li, J. (2019). Network-based feature extraction method for fraud detection via label propagation. 2019 IEEE International Conference on Big Data and Smart Computing (BigComp), 1–6.
CC BY NC ND