Classification of fraud in public biddings through clustering of companies in collusion

• David P. Galvão Júnior UFPB
• Gilberto F. de Sousa Filho UFPB
• Lucídio dos Anjos F. Cabral UFPB

DOI: https://doi.org/10.5753/wcge.2023.229519

Abstract

Bid rigging causes significant damage to society and is the subject of intense investigation by the authorities. Many works try to analyze the financial values of the proposals during the competition of a bidding, trying to classify them a fraud. In this work, we propose to group companies that participate in the same fraudulent bids, quantifying the probability of each group being collusive, and apply this metric in machine learning algorithms to classify new bids. Results demonstrate an improvement in the cross-validation correlation of up to 9% compared to the classification obtained by the literature metrics.

Keywords: collusion detection, machine learning, cluster analysis

References

Bosio, E. and Djankov, S. (2020). How large is public procurement? Breiman, L., Friedman, J., Stone, C., and Olshen, R. (1984). Classification and Regression Trees. Taylor & Francis.

Domingos, P. M. and Pazzani, M. J. (1996). Beyond independence: Conditions for the optimality of the simple bayesian classifier. In International Conference on Machine Learning.

du Boisberranger, J., Van den Bossche, J., Estève, L., and J. Fan, T. (2022). scikit-learn: machine learning in python.

Fawcett, T. (2006). An introduction to roc analysis. Pattern Recognition Letters, 27(8):861–874. ROC Analysis in Pattern Recognition.

Huber, M. and Imhof, D. (2019). Machine learning with screens for detecting bid-rigging cartels. International Journal of Industrial Organization, 65:277–301.

Imhof, D., Blatter, M., Brisset, K., BUhler, S., Egli, A., Karagök, Y., Madı̀, T., Schmutzler, A., and Wyssling, M. (2017). Simple statistical screens to detect bid rigging acknowledgement.

Imhof, D., Karagök, Y., and Rutz, S. (2018). SCREENING FOR BID RIGGING—DOES IT WORK? Journal of Competition Law & Economics, 14(2):235–261.

Langley, P., Iba, and, W., and Thompson, K. (1992). An analysis of bayesian classifiers. In Proceedings of the Tenth National Conference on Artificial Intelligence, AAAI’92, page 223–228. AAAI Press.

Rodríguez, M. J. G., Rodríguez-Montequín, V., Ballesteros-Pérez, P., Love, P. E., and Signor, R. (2022). Collusion detection in public procurement auctions with machine learning algorithms. Automation in Construction, 133.

Sakkis, G., Androutsopoulos, I., Paliouras, G., Karkaletsis, V., Spyropoulos, C. D., and Stamatopoulos, P. (2003). A memory-based approach to anti-spam filtering for mailing lists. Inf. Retr., 6(1):49–73.

Signor, R., Love, P. E. D., Belarmino, A. T. N., and Olatunji, O. A. (2020). Detection of collusive tenders in infrastructure projects: Learning from operation car wash. Journal of Construction Engineering and Management, 146.