Anomaly Detection in Brazilian Federal Government Purchase Cards Through Unsupervised Learning Techniques

Resumo


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.
Palavras-chave: Anomaly detection, Government purchase cards, Unsupervised learning, Complex networks
Publicado
29/11/2021
NUNES, Breno; COLLIRI, Tiago; LAURETTO, Marcelo; LIU, Weiguang; ZHAO, Liang. Anomaly Detection in Brazilian Federal Government Purchase Cards Through Unsupervised Learning Techniques. In: BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS), 10. , 2021, Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . ISSN 2643-6264.