Detección de fraude digital mediante el uso de grafos
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jul 13, 2026
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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
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 con las características de los datos necesarios para operar este tipo de algoritmos. En este sentido, el fraude transaccional en plataformas digitales ha incrementado en años recientes. Los defraudadores recurren a 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.
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, jul. 2026.
ISSN 2395-8782.
Disponible en: <https://cienciaergosum.uaemex.mx/article/view/23217>. Fecha de acceso: 16 ago. 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.
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3n8o
Dou, Y., Liu, Z., Sun, L., Deng, Y., Peng, H., & Yu, P. S. (2020). Enhancing graph neural network-based fraud
detectors against camouflaged fraudsters. Arxiv. https://doi.org/10.1145/3340531.3411903
FATF-Interpol-Egmont Group (2023). Illicit Financial Flows from Cyber-Enabled Fraud. FATF. Paris. https://
www.fatf-gafi.org/content/dam/fatf-gafi/reports/Illicit-financial-flows-cyber-enabled-fraud.pdf.coredownload.
inline.pdf
Fu, K., Cheng, D., Tu, Y., & Zhang, L. (2016). Credit card fraud detection using convolutional neural networks.
Neural Information Processing, 9949, 483-490. Cham: Springer. https://doi.org/10.1007/978-3-319-
46675-0_53
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: 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 Chase (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. Quattrociocchi, A. Sala. (eds.), Complex Networks &
Their Applications V. COMPLEX NETWORKS 2016 2016. Studies in Computational Intelligence,
vol 693. Cham: Springer. https://doi.org/10.1007/978-3-319-50901-3_57
Liu, Z., Chen, C., Yang, X., Zhou, J., Li, X., & Song, L. (2020). Heterogeneous graph neural networks
for malicious account detection. ArXiv. https://doi.org/10.48550/arXiv.2002.12307
Maes, 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 THECHNOLOGIES January 16-19, 2002 (Havana, Cuba). http://como.vub.ac.be/
Members/bram.htm
Malone, C. (2023). Online payment fraud: Market forecasts, emerging threats and segment analysis 2023-
2028. 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. https://doi.org/10.1088/1742-6596/1601/5/052016
Rossi, E., Chamberlain, B., Frasca, F., Eynard, D., Monti, F., & Bronstein, M. (2020). Temporal graph
networks for deep learning on dynamic graphs, Arxiv. https://doi.org/10.48550/arXiv.2006.10637
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. https://doi.org/10.48550/arXiv.2006.10637
Ravelin. (2024). Ravelin Insights. 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.
OʼReilly Media.
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). 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. New York: Association for Computing Machinery. 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
(pp. 5519-5522). https://doi.org/10.1109/WICOM.2007.1352
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)
(pp. 1-6). 10.1109/BIGCOMP.2019.8679414