Comparativo de técnicas de inteligência artificial explicável na detecção de fraudes em transações com cartão de crédito
Resumo
Sistemas inteligentes são utilizados no mercado financeiro, inclusive para detecção de fraudes. Em transações com cartões de crédito, algoritmos de aprendizado de máquina podem ser usados para obter modelos que automatizam decisões como classificar uma transação como fraudulenta ou não. Neste contexto, este trabalho apresenta uma comparação entre as técnicas de inteligência artificial explicável SHAP e LIME em modelos para detecção de fraudes em transações com cartão crédito, mostrando que essas técnicas podem ser adequadas ao problema. Também é discutida a utilização de algoritmos naturalmente explicáveis, assim como a efetividade e a necessidade de técnicas de inteligência artificial explicável.Referências
Alfaiz, N. S. and Fati, S. M. (2022). Enhanced credit card fraud detection model using machine learning. Electronics (Switzerland), 11.
Alvarez-Melis, D. and Jaakkola, T. S. (2018). On the robustness of interpretability methods. [link].
Bourdonnaye, F. D. L. and Daniel, F. (2021). Evaluating categorical encoding methods on a real credit card fraud detection database. [link].
Bussmann, N., Giudici, P., Marinelli, D., and Papenbrock, J. (2021). Explainable machine learning in credit risk management. Computational Economics, 57:203–216.
Carcillo, F., Dal Pozzolo, A., Le Borgne, Y.-A., Caelen, O., Mazzer, Y., and Bontempi, G. (2017). Scarff : a scalable framework for streaming credit card fraud detection with spark. Information Fusion, 41.
Carcillo, F., Le Borgne, Y.-A., Caelen, O., Kessaci, Y., Oblé, F., and Bontempi, G. (2019). Combining unsupervised and supervised learning in credit card fraud detection. Information Sciences.
Chaquet-Ulldemolins, J., Gimeno-Blanes, F.-J., Moral-Rubio, S., Muñoz-Romero, S., and Rojo-Álvarez, J.-L. (2022). On the black-box challenge for fraud detection using machine learning (ii): Nonlinear analysis through interpretable autoencoders. Applied Sciences, 12(8).
Chaudhary, K., Yadav, J., and Mallick, B. (2012). A review of fraud detection techniques: Credit card. International Journal of Computer Applications, 45:975–8887.
Dal Pozzolo, A., Boracchi, G., Caelen, O., Alippi, C., and Bontempi, G. (2017). Credit card fraud detection: A realistic modeling and a novel learning strategy. IEEE Transactions on Neural Networks and Learning Systems, PP:1–14.
Dal Pozzolo, A., Caelen, O., Le Borgne, Y.-A., Waterschoot, S., and Bontempi, G. (2014). Learned lessons in credit card fraud detection from a practitioner perspective. Expert Systems with Applications, 41:4915–4928.
Doshi-Velez, F. and Kim, B. (2017). Towards a rigorous science of interpretable machine learning. [link].
European Union (2016). Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation). Official Journal L110, 59:1–88.
Gee, J., Button, M., and Brooks, G. (2019). The financial cost of fraud: what data from around the world shows. MacIntyre Hudson. Institution: University of Portsmouth. Department: Institute of Criminal Justice Studies.
Hanif, A. (2021). Towards explainable artificial intelligence in banking and financial services. [link].
Hsin, Y.-Y., Dai, T.-S., Ti, Y.-W., and Huang, M.-C. (2021). Interpretable electronic transfer fraud detection with expert feature constructions. In CIKM Workshops.
Ji, Y. (2021). Explainable ai methods for credit card fraud detection: Evaluation of LIME and SHAP through a user study. [link].
Le Borgne, Y.-A., Siblini, W., Lebichot, B., and Bontempi, G. (2022). Reproducible Machine Learning for Credit Card Fraud Detection - Practical Handbook. Université Libre de Bruxelles.
Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Proceedings of the 31st International Conference on Neural Information Anais Estendidos do SBSeg 2024: WTICG 11 Processing Systems, NIPS’17, page 4768–4777, Red Hook, NY, USA. Curran Associates Inc.
Makki, S., Assaghir, Z., Taher, Y., Haque, R., Hacid, M. S., and Zeineddine, H. (2019). An experimental study with imbalanced classification approaches for credit card fraud detection. IEEE Access, 7:93010–93022.
Miller, T. (2023). Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven decision support. [link].
Miller, T., Howe, P., and Sonenberg, L. (2017). Explainable ai: Beware of inmates running the asylum. [link].
Moepya, S. O., Akhoury, S. S., Nelwamondo, F. V., and Twala, B. (2016). The role of imputation in detecting fraudulent financial reporting. International Journal of Innovative Computing, Information and Control ICIC International c, 12:333–356.
Molnar, C. (2022). Interpretable Machine Learning. 2 edition. [link].
Padhi, I., Schiff, Y., Melnyk, I., Rigotti, M., Mroueh, Y., Dognin, P., Ross, J., Nair, R., and Altman, E. (2021). Tabular transformers for modeling multivariate time series. In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 3565–3569. IEEE.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.
Pozzolo, A. D., Caelen, O., Johnson, R. A., and Bontempi, G. (2015). Calibrating probability with undersampling for unbalanced classification. In 2015 IEEE Symposium Series on Computational Intelligence, pages 159–166.
Psychoula, I., Gutmann, A., Mainali, P., Lee, S. H., Dunphy, P., and Petitcolas, F. (2021). Explainable machine learning for fraud detection. Computer, 54(10):49–59.
Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). ”why should i trust you?”explaining the predictions of any classifier. volume 13-17-August-2016, pages 1135–1144. Association for Computing Machinery.
Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5):206–215.
Vilone, G. and Longo, L. (2020). Explainable artificial intelligence: a systematic review. [link].
Wu, T.-Y. and Wang, Y.-T. (2021). Locally interpretable one-class anomaly detection for credit card fraud detection. In 2021 International Conference on Technologies and Applications of Artificial Intelligence (TAAI), pages 25–30.
Alvarez-Melis, D. and Jaakkola, T. S. (2018). On the robustness of interpretability methods. [link].
Bourdonnaye, F. D. L. and Daniel, F. (2021). Evaluating categorical encoding methods on a real credit card fraud detection database. [link].
Bussmann, N., Giudici, P., Marinelli, D., and Papenbrock, J. (2021). Explainable machine learning in credit risk management. Computational Economics, 57:203–216.
Carcillo, F., Dal Pozzolo, A., Le Borgne, Y.-A., Caelen, O., Mazzer, Y., and Bontempi, G. (2017). Scarff : a scalable framework for streaming credit card fraud detection with spark. Information Fusion, 41.
Carcillo, F., Le Borgne, Y.-A., Caelen, O., Kessaci, Y., Oblé, F., and Bontempi, G. (2019). Combining unsupervised and supervised learning in credit card fraud detection. Information Sciences.
Chaquet-Ulldemolins, J., Gimeno-Blanes, F.-J., Moral-Rubio, S., Muñoz-Romero, S., and Rojo-Álvarez, J.-L. (2022). On the black-box challenge for fraud detection using machine learning (ii): Nonlinear analysis through interpretable autoencoders. Applied Sciences, 12(8).
Chaudhary, K., Yadav, J., and Mallick, B. (2012). A review of fraud detection techniques: Credit card. International Journal of Computer Applications, 45:975–8887.
Dal Pozzolo, A., Boracchi, G., Caelen, O., Alippi, C., and Bontempi, G. (2017). Credit card fraud detection: A realistic modeling and a novel learning strategy. IEEE Transactions on Neural Networks and Learning Systems, PP:1–14.
Dal Pozzolo, A., Caelen, O., Le Borgne, Y.-A., Waterschoot, S., and Bontempi, G. (2014). Learned lessons in credit card fraud detection from a practitioner perspective. Expert Systems with Applications, 41:4915–4928.
Doshi-Velez, F. and Kim, B. (2017). Towards a rigorous science of interpretable machine learning. [link].
European Union (2016). Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation). Official Journal L110, 59:1–88.
Gee, J., Button, M., and Brooks, G. (2019). The financial cost of fraud: what data from around the world shows. MacIntyre Hudson. Institution: University of Portsmouth. Department: Institute of Criminal Justice Studies.
Hanif, A. (2021). Towards explainable artificial intelligence in banking and financial services. [link].
Hsin, Y.-Y., Dai, T.-S., Ti, Y.-W., and Huang, M.-C. (2021). Interpretable electronic transfer fraud detection with expert feature constructions. In CIKM Workshops.
Ji, Y. (2021). Explainable ai methods for credit card fraud detection: Evaluation of LIME and SHAP through a user study. [link].
Le Borgne, Y.-A., Siblini, W., Lebichot, B., and Bontempi, G. (2022). Reproducible Machine Learning for Credit Card Fraud Detection - Practical Handbook. Université Libre de Bruxelles.
Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Proceedings of the 31st International Conference on Neural Information Anais Estendidos do SBSeg 2024: WTICG 11 Processing Systems, NIPS’17, page 4768–4777, Red Hook, NY, USA. Curran Associates Inc.
Makki, S., Assaghir, Z., Taher, Y., Haque, R., Hacid, M. S., and Zeineddine, H. (2019). An experimental study with imbalanced classification approaches for credit card fraud detection. IEEE Access, 7:93010–93022.
Miller, T. (2023). Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven decision support. [link].
Miller, T., Howe, P., and Sonenberg, L. (2017). Explainable ai: Beware of inmates running the asylum. [link].
Moepya, S. O., Akhoury, S. S., Nelwamondo, F. V., and Twala, B. (2016). The role of imputation in detecting fraudulent financial reporting. International Journal of Innovative Computing, Information and Control ICIC International c, 12:333–356.
Molnar, C. (2022). Interpretable Machine Learning. 2 edition. [link].
Padhi, I., Schiff, Y., Melnyk, I., Rigotti, M., Mroueh, Y., Dognin, P., Ross, J., Nair, R., and Altman, E. (2021). Tabular transformers for modeling multivariate time series. In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 3565–3569. IEEE.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.
Pozzolo, A. D., Caelen, O., Johnson, R. A., and Bontempi, G. (2015). Calibrating probability with undersampling for unbalanced classification. In 2015 IEEE Symposium Series on Computational Intelligence, pages 159–166.
Psychoula, I., Gutmann, A., Mainali, P., Lee, S. H., Dunphy, P., and Petitcolas, F. (2021). Explainable machine learning for fraud detection. Computer, 54(10):49–59.
Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). ”why should i trust you?”explaining the predictions of any classifier. volume 13-17-August-2016, pages 1135–1144. Association for Computing Machinery.
Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5):206–215.
Vilone, G. and Longo, L. (2020). Explainable artificial intelligence: a systematic review. [link].
Wu, T.-Y. and Wang, Y.-T. (2021). Locally interpretable one-class anomaly detection for credit card fraud detection. In 2021 International Conference on Technologies and Applications of Artificial Intelligence (TAAI), pages 25–30.
Publicado
16/09/2024
Como Citar
LIMA, Gabriel Mendes de; PISANI, Paulo Henrique.
Comparativo de técnicas de inteligência artificial explicável na detecção de fraudes em transações com cartão de crédito. In: WORKSHOP DE TRABALHOS DE INICIAÇÃO CIENTÍFICA E DE GRADUAÇÃO - SIMPÓSIO BRASILEIRO DE SEGURANÇA DA INFORMAÇÃO E DE SISTEMAS COMPUTACIONAIS (SBSEG), 24. , 2024, São José dos Campos/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 244-255.
DOI: https://doi.org/10.5753/sbseg_estendido.2024.243180.
