A multi-agent data mining system for cartel detection in Brazilian government procurement

The main focus of this research project is the problem of extracting useful information from the Brazilian federal procurement process databases used by government auditors in the process of corruption detection and prevention to identify cartel formation among applicants. Extracting useful information to enhance cartel detection is a complex problem from many perspectives due to the large volume of data used to correlate information and the dynamic and diversified strategies companies use to hide their fraudulent operations. To attack the problem of data volume, we have used two data mining model functions, clustering and association rules, and a multi-agent approach to address the dynamic strategies of companies that are involved in cartel formation. To integrate both solutions, we have developed AGMI, an agent-mining tool that was validated using real data from the Brazilian Office of the Comptroller General, an institution of government auditing, where several measures are currently used to prevent and fight corruption. Our approach resulted in explicit knowledge discovery because AGMI presented many association rules that provided a 90% correct identification of cartel formation, according to expert assessment. According to auditing specialists, the extracted knowledge could help in the detection, prevention and monitoring of cartels that act in public procurement processes.

► We worked with the subject of detecting fraud in the use of federal public funds.
► The research focus is to find mechanisms to prevent cartel formation in procurement processes.
► We defined and developed AGMI an agent-mining tool that integrates multi-agent into distributed data mining techniques.
► AGMI was applied to the Brazilian government procurement domain using real data for knowledge discovery.
► AGMI performed well with respect to the quality of the rules discovered with the Rule Quality average at approximately 90%.