A decision support system for fraud detection in public procurement

Abstract

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.

Review of Public Procurement Fraud Detection Techniques Powered by Emerging Technologies

“…The most commonly used methods in the studies are linear and logistic regression, neural networks, and Naive Bayes algorithms since they are most used for classification and clustering. Namely, models are fitted on historical data and move in the direction of an early warning system that can provide pre-determined supervisory bodies with insights into the risks associated with concluding contracts with risky economic operators [1,18,21] or can identify potential cartels or collusion behavior using associative rules or graph databases algorithms to see the relationships between economic operators and eventually their daughter companies [12,25,26,29,32,43].…”

“…One of the essential segments and research questions is certainly the input data. The studies are focused on the detection and analysis of high-quality corruption indicators, risk patterns, or red flags as representatives of corruptive or suspicious actions with the aim of developing models with the best predictive features [3,4,5,6,22,24,31,32]. Including different databases, pattern recognition, and elicitation knowledge is part of the Knowledge Discovery area.…”

“…Including different databases, pattern recognition, and elicitation knowledge is part of the Knowledge Discovery area. In short, studies have suggested that by applying the Big Data approach and data mining methods, better results will be achieved, and better indicators can be found [6,32].…”

“…More than 200 different indicators are known so far and are used as variables in algorithms, models, or techniques to perform some of the identified objectives in Table IV [31,32,33]. Due to space limitations, a few of them will be mentioned, as follows:…”

“…Brazil's decision support system for fraud detection in public procurement is a robust tool implemented with the aim of systematic analysis and the identification of the main risk patterns, such as collusion between bidders, conflicts of interest, and risk companies using algorithms such as graph theory, clustering, and regression analysis with advanced data science methods [32]. A similar tool was developed in Africa, named Tendersure (https://www.tendersure.co.ke/), which is based on web technology but does not use advanced technologies and tools such as the system from Brazil.…”