Abstract
The Federal Government Purchase Card (CPGF) has been used in Brazil since 2005, allowing agencies and entities of the federal public administration to make purchases of material and provision of services through this method. Although this payment system offers several advances, in the technological and administrative aspect, it is also susceptible to possible cases of card misuse and, consequently, waste of public funds, in the form of purchases that do not comply with the terms of the current legislation. In this work, we approach this problem by testing and evaluating unsupervised learning techniques on detecting anomalies in CPGF historical data. Four different methods are considered for this task: K-means, agglomerative clustering, a network-based approach, which is also introduced in this study, and a hybrid model. The experimental results obtained indicate that unsupervised methods, in particular the network-based approach, can indeed help in the task of monitoring government purchase card expenses, by flagging suspect transactions for further investigation without requiring the presence of a specialist in this process.
This research was funded by CEPID-CeMEAI – Center for Mathematical Sciences Applied to Industry (grant 2013/07375-0, Sao Paulo Research Foundation–FAPESP), and was carried out at the Center for Artificial Intelligence (C4AI-USP), with also support from the Sao Paulo Research Foundation (FAPESP) under grant number: 2019/07665-4 and by the IBM Corporation. This work is also supported in part by FAPESP under grant numbers 2015/50122-0, the Brazilian National Council for Scientific and Technological Development (CNPq) under grant number 303199/2019-9, and the Ministry of Science and Technology of China under grant number: G20200226015
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Nunes, B., Colliri, T., Lauretto, M., Liu, W., Zhao, L. (2021). Anomaly Detection in Brazilian Federal Government Purchase Cards Through Unsupervised Learning Techniques. In: Britto, A., Valdivia Delgado, K. (eds) Intelligent Systems. BRACIS 2021. Lecture Notes in Computer Science(), vol 13074. Springer, Cham. https://doi.org/10.1007/978-3-030-91699-2_2
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