Using Bayesian Networks to Identify and Prevent Split Purchases in Brazil
Abstract
To cope with society’s demand for transparency and corruption prevention, the Brazilian Office of the Comptroller General (CGU) has carried out a number of actions, including: awareness cam-paigns aimed at the private sector; campaigns to educate the public; research initiatives; and regular inspections and audits of municipalities and states. Although CGU has collected infor-mation from various different sources – Rev-enue Agency, Federal Police, and others –, going through all the data in order to find suspicious transactions has proven to be really challenging. In this paper, we present a Data Mining study ap-plied on real data – government purchases – for finding transactions that might become irregular before they are considered as such in order to act proactively. Moreover, we compare the perfor-mance of various Bayesian Network (BN) learn-ing algorithms with different parameters in order to fine tune the learned models and improve their performance. The best result was obtained us-ing the Tree Augmented Network (TAN) algo-rithm and oversampling the minority class in or-der to balance the data set. Using a 10-fold cross-validation, the model correctly classified all split purchases, it obtained a ROC area of.999, and its accuracy was 99.197%. 1
Cite
Text
Carvalho et al. "Using Bayesian Networks to Identify and Prevent Split Purchases in Brazil." Conference on Uncertainty in Artificial Intelligence, 2014.Markdown
[Carvalho et al. "Using Bayesian Networks to Identify and Prevent Split Purchases in Brazil." Conference on Uncertainty in Artificial Intelligence, 2014.](https://mlanthology.org/uai/2014/carvalho2014uai-using/)BibTeX
@inproceedings{carvalho2014uai-using,
title = {{Using Bayesian Networks to Identify and Prevent Split Purchases in Brazil}},
author = {Carvalho, Rommel N. and Sales, Leonardo and Da Rocha, Henrique A. and Mendes, Gilson Libório},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2014},
pages = {70-78},
url = {https://mlanthology.org/uai/2014/carvalho2014uai-using/}
}