Delator: Detecção Automática de Indícios de Lavagem de Dinheiro por Redes Neurais em Grafos de Transações
Resumo
A lavagem de dinheiro é hoje uma das atividades criminais mais relevantes, principalmente devido ao seu potencial de provocar grandes perdas financeiras para governos, bancos, etc. Nós propomos o DELATOR, uma nova CAAT (tecnologia de auditoria assistida por computador) para detectar atividades de lavagem de dinheiro baseada em modelos de rede neural que codificam transferências bancárias como um grafo temporal de larga escala. Em colaboração com um banco brasileiro, projetamos e aplicamos uma estratégia de avaliação para quantificar o desempenho do DELATOR em dados históricos que englobam milhões de clientes. O DELATOR supera uma solução off-the-shelf da Amazon AWS em 18.9% em termos de AUC. Conduzimos experimentos reais que levaram à descoberta de 8 novos casos suspeitos dentre 100 analisados, que seriam reportados às autoridades sob os critérios atuais.
Palavras-chave:
redes neurais em grafos, detecção de lavagem de dinheiro, aprendizado multi-tarefa, grafos dinâmicos de Dinheiro
Referências
Assumpção, H. S., Souza, F., Lacerda Campos, L., Castro Pires, V. T., Laurentys de Almeida, P. M., and Murai, F. (2022). Delator: Automatic detection of money laundering evidence on transaction graphs via neural networks. arXiv preprint arXiv:2205.10293.
BACEN (2020a). Circular nº 3.978, de 23 de janeiro de 2020.
BACEN (2020b). Circular nº 4.001, de 29 de janeiro de 2020.
FATF (2012-2021). International standards on combating money laundering and the financing of terrorism & proliferation.
Fernández, A., Garcia, S., Herrera, F., and Chawla, N. V. (2018). Smote for learning from imbalanced data: progress and challenges, marking the 15-year anniversary. JAIR, 61:863–905.
Gopinathan, K. M., Biafore, L. S., Ferguson, W. M., Lazarus, M. A., Pathria, A. K., and Jost, A. (1998). Fraud detection using predictive modeling. US Patent 5,819,226.
Halbouni, S. S., Obeid, N., and Garbou, A. (2016). Corporate governance and information technology in fraud prevention and detection. Managerial Auditing Journal.
Hamilton, W., Ying, Z., and Leskovec, J. (2017). Inductive Representation Learning on Large Graphs. In NeurIPS, volume 30.
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. In NeurIPS, volume 30.
Liang, C., Liu, Z., Liu, B., Zhou, J., Li, X., Yang, S., and Qi, Y. (2019). Uncovering insurance fraud conspiracy with network learning. In SIGIR, pages 1181–1184.
Liu, Y., Ao, X., Qin, Z., Chi, J., Feng, J., Yang, H., and He, Q. (2021). Pick and choose: A gnn-based imbalanced learning approach for fraud detection. In WWW, pages 3168– 3177.
Othman, R., Aris, N. A., Mardziyah, A., Zainan, N., and Amin, N. M. (2015). Fraud detection and prevention methods in the malaysian public sector: Accountants’ and internal auditors’ perceptions. Procedia Economics and Finance, 28:59–67.
Pareja, A., Domeniconi, G., Chen, J., Ma, T., Suzumura, T., Kanezashi, H., Kaler, T., Schardl, T. B., and Leiserson, C. E. (2020). EvolveGCN: Evolving graph convolutional networks for dynamic graphs. In AAAI.
Pereira, R. and Murai, F. (2021). Quão efetivas são redes neurais baseadas em grafos na detecção de fraude para dados em rede? In BraSNAM, pages 205–210.
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., and Gulin, A. (2018). Catboost: Unbiased boosting with categorical features. In NeurIPS, page 6639–6649.
Radford, A., Narasimhan, K., Salimans, T., and Sutskever, I. (2018). Improving language understanding by generative pre-training.
Wang, D., Lin, J., Cui, P., Jia, Q., Wang, Z., Fang, Y., Yu, Q., Zhou, J., Yang, S., and Qi, Y. (2019). A semi-supervised graph attentive network for financial fraud detection. In ICDM, pages 598–607.
Wang, J., Zhang, S., Xiao, Y., and Song, R. (2021). A review on graph neural network methods in financial applications.
Weber, M., Domeniconi, G., Chen, J., Weidele, D. K. I., Bellei, C., Robinson, T., and Leiserson, C. E. (2019). Anti-money laundering in bitcoin: Experimenting with graph convolutional networks for financial forensics. arXiv preprint arXiv:1908.02591.
Widuri, R. and Gautama, Y. (2020). Computer-assisted audit techniques (caats) for financial fraud detection: A qualitative approach. In ICIMTech, pages 771–776.
Zhao, T., Zhang, X., and Wang, S. (2021). Graphsmote: Imbalanced node classification on graphs with graph neural networks. In WSDM, pages 833–841.
Zheng, D., Song, X., Ma, C., Tan, Z., Ye, Z., Dong, J., Xiong, H., Zhang, Z., and Karypis, G. (2020). Dgl-ke: Training knowledge graph embeddings at scale. In SIGIR, pages 739–748.
Zhu, Y.-N., Luo, X., Li, Y.-F., Bu, B., Zhou, K., Zhang, W., and Lu, M. (2020). Heterogeneous mini-graph neural network and its application to fraud invitation detection. In ICDM, pages 891–899.
BACEN (2020a). Circular nº 3.978, de 23 de janeiro de 2020.
BACEN (2020b). Circular nº 4.001, de 29 de janeiro de 2020.
FATF (2012-2021). International standards on combating money laundering and the financing of terrorism & proliferation.
Fernández, A., Garcia, S., Herrera, F., and Chawla, N. V. (2018). Smote for learning from imbalanced data: progress and challenges, marking the 15-year anniversary. JAIR, 61:863–905.
Gopinathan, K. M., Biafore, L. S., Ferguson, W. M., Lazarus, M. A., Pathria, A. K., and Jost, A. (1998). Fraud detection using predictive modeling. US Patent 5,819,226.
Halbouni, S. S., Obeid, N., and Garbou, A. (2016). Corporate governance and information technology in fraud prevention and detection. Managerial Auditing Journal.
Hamilton, W., Ying, Z., and Leskovec, J. (2017). Inductive Representation Learning on Large Graphs. In NeurIPS, volume 30.
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. In NeurIPS, volume 30.
Liang, C., Liu, Z., Liu, B., Zhou, J., Li, X., Yang, S., and Qi, Y. (2019). Uncovering insurance fraud conspiracy with network learning. In SIGIR, pages 1181–1184.
Liu, Y., Ao, X., Qin, Z., Chi, J., Feng, J., Yang, H., and He, Q. (2021). Pick and choose: A gnn-based imbalanced learning approach for fraud detection. In WWW, pages 3168– 3177.
Othman, R., Aris, N. A., Mardziyah, A., Zainan, N., and Amin, N. M. (2015). Fraud detection and prevention methods in the malaysian public sector: Accountants’ and internal auditors’ perceptions. Procedia Economics and Finance, 28:59–67.
Pareja, A., Domeniconi, G., Chen, J., Ma, T., Suzumura, T., Kanezashi, H., Kaler, T., Schardl, T. B., and Leiserson, C. E. (2020). EvolveGCN: Evolving graph convolutional networks for dynamic graphs. In AAAI.
Pereira, R. and Murai, F. (2021). Quão efetivas são redes neurais baseadas em grafos na detecção de fraude para dados em rede? In BraSNAM, pages 205–210.
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., and Gulin, A. (2018). Catboost: Unbiased boosting with categorical features. In NeurIPS, page 6639–6649.
Radford, A., Narasimhan, K., Salimans, T., and Sutskever, I. (2018). Improving language understanding by generative pre-training.
Wang, D., Lin, J., Cui, P., Jia, Q., Wang, Z., Fang, Y., Yu, Q., Zhou, J., Yang, S., and Qi, Y. (2019). A semi-supervised graph attentive network for financial fraud detection. In ICDM, pages 598–607.
Wang, J., Zhang, S., Xiao, Y., and Song, R. (2021). A review on graph neural network methods in financial applications.
Weber, M., Domeniconi, G., Chen, J., Weidele, D. K. I., Bellei, C., Robinson, T., and Leiserson, C. E. (2019). Anti-money laundering in bitcoin: Experimenting with graph convolutional networks for financial forensics. arXiv preprint arXiv:1908.02591.
Widuri, R. and Gautama, Y. (2020). Computer-assisted audit techniques (caats) for financial fraud detection: A qualitative approach. In ICIMTech, pages 771–776.
Zhao, T., Zhang, X., and Wang, S. (2021). Graphsmote: Imbalanced node classification on graphs with graph neural networks. In WSDM, pages 833–841.
Zheng, D., Song, X., Ma, C., Tan, Z., Ye, Z., Dong, J., Xiong, H., Zhang, Z., and Karypis, G. (2020). Dgl-ke: Training knowledge graph embeddings at scale. In SIGIR, pages 739–748.
Zhu, Y.-N., Luo, X., Li, Y.-F., Bu, B., Zhou, K., Zhang, W., and Lu, M. (2020). Heterogeneous mini-graph neural network and its application to fraud invitation detection. In ICDM, pages 891–899.
Publicado
31/07/2022
Como Citar
ASSUMPÇÃO, Henrique S.; SOUZA, Fabrício; CAMPOS, Leandro Lacerda; PIRES, Vinícius T. de Castro; ALMEIDA, Paulo M. Laurentys de; MURAI, Fabrício.
Delator: Detecção Automática de Indícios de Lavagem de Dinheiro por Redes Neurais em Grafos de Transações. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 11. , 2022, Niterói.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 13-24.
ISSN 2595-6094.
DOI: https://doi.org/10.5753/brasnam.2022.223137.
