Identifying IT Purchases Anomalies in the Brazilian Government Procurement System Using Deep Learning

Abstract

The Department of Research and Strategic Information (DIE), from the Brazilian Office of the Comptroller General (CGU), is responsible for investigating potential problems related to federal expenditures. To pursue this goal, DIE regularly has to analyze large volumes of data to search for anomalies that can reveal suspicious activities. With the growing demand from the citizens for transparency and corruption prevention, DIE is constantly looking for new methods to automate these processes. In this work, we investigate IT purchases anomalies in the Federal Government Procurement System by using a deep learning algorithm to generate a predictive model. This model will be used to prioritize actions carried out by the office in its pursuit of problems related to this kind of purchases. The data mining process followed the CRISP-DM methodology and the modeling phase tested the parallel resources of the H2O tool. We evaluated the performance of twelve deep learning with auto-encoder models, each one generated under a different set of parameters, in order to find the best input data reconstruction model. The best model achieved a mean squared error (MSE) of 0.0012775 and was used to predict the anomalies over the test file samples.