A decision support system for fraud detection in public procurement
Velasco, Rafael B.;Carpanese, Igor;Interian, Ruben;Paulo Neto, Octavio C. G.;Ribeiro, Celso C.
Institute of Computing Universidade Federal Fluminense Niterói RJ 24210‐346 Brazil
GAECO – Paraíba Prosecutor's Office and Federal Prosecutor's Office Brasília DF 70070‐925 Brazil
Consultant Praia do Flamengo 66B Rio de Janeiro RJ 22210‐030 Brazil
decision support system;data mining;data science;fraud detection;risk patterns;public procurement;corruption risk assessment;COVID-19
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
CNPqNational Council for Scientific and Technological Development (CNPq) [303958/2015-4, 425778/2016-9]; FAPERJ research grantCarlos Chagas Filho Foundation for Research Support of the State of Rio de Janeiro (FAPERJ) [E-26/202.854/2017]; Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)CAPES 
Over the past few years, investigators in Brazil have been uncovering numerous corruption and money laundering schemes at all levels of government and in the country's largest corporations. It is estimated that between 2% and 5% of the global GDP is lost annually because of such practices, not only directly impacting public services and private sector development but also strengthening organized crime. However, most law enforcement agencies do not have the capability to carry out systematic corruption risk assessment leveraging on the availability of data related to public procurement. The currently prevailing approach employed by Brazilian law enforcement agencies to detect companies involved in potential cases of fraud consists in receiving circumstantial evidence or complaints from whistleblowers. As a result, a large number of companies involved in fraud remain undetected and unprosecuted. The decision support system (DSS) described in this work addresses these existing limitations by providing a tool for systematic analysis of public procurement. It allows the law enforcement agencies to establish priorities concerning the companies to be investigated. This DSS incorporates data mining algorithms for quantifying dozens of corruption risk patterns for all public contractors inside a specific jurisdiction, leading to improvements in the quality of public spending and to the identification of more cases of fraud. These algorithms combine operations research tools such as graph theory, clusterization, and regression analysis with advanced data science methods to allow the identification of the main risk patterns, such as collusion between bidders, conflicts of interest (e.g., a politician who owns a company contracted by the same government body where he or she was elected), and companies owned by a potentially straw person used for disguising its real owner (e.g., beneficiaries of cash conditional transfer programs). The DSS has already led to a detailed analysis of large public procurement datasets, which add up to more than 50 billion dollars. Moreover, the DSS provided strategic inputs to investigations conducted by federal and state agencies.