Multicriteria Anomaly Detection in Government Purchases
Resumo
Government purchases are the usual instrument for public acquisition of goods and services. Despite extensive legislation and several control and auditing mechanisms, frauds are still diverse and commonplace at all levels of public administration. This work proposes a methodology for detecting anomalies in government purchases. The methodology promotes several levels of filtering with respect to entities involved and purchases considered as fraudulent considering diverse criteria. The applicability and effectiveness of the methodology is demonstrated through a case study using real data where we were able to identify a long term provider collusion.
Palavras-chave:
anomaly detection, government purchases
Referências
Agrawal, R. and Srikant, R. Fast algorithms for mining association rules in large databases. In Proceedings of the 20th International Conference on Very Large Data Bases. VLDB ’94. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp. 487–499, 1994.
Balaniuk, R., Bessiere, P., Mazer, E., and Cobbe, P. Corruption risk analysis using semi-supervised naïve bayes classifiers. International Journal of Reasoning-based Intelligent Systems vol. 5, pp. 237 – 245, 2013.
Fraga, A. Detecção de Casos Suspeitos de Fraude em Licitações Realizadas no Município da Paraíba. Ph.D. thesis, Universidade Federal da Paraíba, Brasil, 2017.
Ghedini Ralha, C. and Sarmento Silva, C. V. A multi-agent data mining system for cartel detection in brazilian government procurement. Expert Syst. Appl. 39 (14): 11642–11656, Oct., 2012.
Grilo Junior, T. Aplicação de Técnicas de Data Mining para Auxiliar o Processo de Fiscalização. Ph.D. thesis, Universidade Federal da Paraíba, 2010.
Gutflaish, E., Kontorovich, A., Sabato, S., Biller, O., and Sofer, O. Temporal anomaly detection: calibrating the surprise. CoRR vol. abs/1705.10085, pp. 1705.10085, 2017.
Hallac, D., Vare, S., Boyd, S. P., and Leskovec, J. Toeplitz inverse covariance-based clustering of multivariate time series data. CoRR vol. abs/1706.03161, pp. 1706.03161, 2017.
Siddiqui, M. A., Fern, A., Dietterich, T. G., Wright, R., Theriault, A., and Archer, D. W. Feedback-guided anomaly discovery via online optimization. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’18. ACM, New York, NY, USA, pp. 2200–2209, 2018.
Song, D., Xia, N., Cheng, W., Chen, H., and Tao, D. Deep r-th root of rank supervised joint binary embedding for multivariate time series retrieval. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’18. ACM, New York, NY, USA, pp. 2229–2238, 2018.
Tian, K., Zhou, S., Fan, J., and Guan, J. Learning competitive and discriminative reconstructions for anomaly detection. CoRR vol. abs/1903.07058, pp. 1903.07058, 2019.
YAGOUBI, D. E., Akbarinia, R., Kolev, B., Levchenko, O., Masseglia, F., Valduriez, P., and Shasha, D. Parcorr: efficient parallel methods to identify similar time series pairs across sliding windows. Data Mining and Knowledge Discovery vol. 32, 08, 2018.
Balaniuk, R., Bessiere, P., Mazer, E., and Cobbe, P. Corruption risk analysis using semi-supervised naïve bayes classifiers. International Journal of Reasoning-based Intelligent Systems vol. 5, pp. 237 – 245, 2013.
Fraga, A. Detecção de Casos Suspeitos de Fraude em Licitações Realizadas no Município da Paraíba. Ph.D. thesis, Universidade Federal da Paraíba, Brasil, 2017.
Ghedini Ralha, C. and Sarmento Silva, C. V. A multi-agent data mining system for cartel detection in brazilian government procurement. Expert Syst. Appl. 39 (14): 11642–11656, Oct., 2012.
Grilo Junior, T. Aplicação de Técnicas de Data Mining para Auxiliar o Processo de Fiscalização. Ph.D. thesis, Universidade Federal da Paraíba, 2010.
Gutflaish, E., Kontorovich, A., Sabato, S., Biller, O., and Sofer, O. Temporal anomaly detection: calibrating the surprise. CoRR vol. abs/1705.10085, pp. 1705.10085, 2017.
Hallac, D., Vare, S., Boyd, S. P., and Leskovec, J. Toeplitz inverse covariance-based clustering of multivariate time series data. CoRR vol. abs/1706.03161, pp. 1706.03161, 2017.
Siddiqui, M. A., Fern, A., Dietterich, T. G., Wright, R., Theriault, A., and Archer, D. W. Feedback-guided anomaly discovery via online optimization. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’18. ACM, New York, NY, USA, pp. 2200–2209, 2018.
Song, D., Xia, N., Cheng, W., Chen, H., and Tao, D. Deep r-th root of rank supervised joint binary embedding for multivariate time series retrieval. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’18. ACM, New York, NY, USA, pp. 2229–2238, 2018.
Tian, K., Zhou, S., Fan, J., and Guan, J. Learning competitive and discriminative reconstructions for anomaly detection. CoRR vol. abs/1903.07058, pp. 1903.07058, 2019.
YAGOUBI, D. E., Akbarinia, R., Kolev, B., Levchenko, O., Masseglia, F., Valduriez, P., and Shasha, D. Parcorr: efficient parallel methods to identify similar time series pairs across sliding windows. Data Mining and Knowledge Discovery vol. 32, 08, 2018.
Publicado
07/10/2019
Como Citar
MAIA, Patrícia; MEIRA JR., Wagner; BARBOSA, Breno; CRUZ, Gustavo.
Multicriteria Anomaly Detection in Government Purchases. In: SYMPOSIUM ON KNOWLEDGE DISCOVERY, MINING AND LEARNING (KDMILE) , 2019, Fortaleza.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 97-104.
ISSN 2763-8944.
DOI: https://doi.org/10.5753/kdmile.2019.8794.
