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Fraud, corruption, and collusion are the most common types of crime in public procurement processes; they produce significant monetary losses, inefficiency, and misuse of the public treasury. However, empirical research in this area to detect these crimes is still insufficient. This article presents a systematic literature review focusing on the most contemporary data-driven techniques applied to crime detection in public procurement. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology was adopted to identify typical elements that lead to crimes in public contracting. We collected scientific papers and analyzed the selected research using the Scopus repository. We evaluated and summarized findings related to crime detection techniques based mainly on machine learning and network science, as well as studies using fraud risk indices. Some methodologies presented promising results in identifying crimes, especially those using labeled data and machine learning techniques. However, due to the frequent unavailability of pre-labeled data on past cases, analysis through network science tools has become more evident and relevant in exploratory research.

Fraud in public funding can have deleterious consequences for societies’ economic, social, and political well-being. Fraudulent activity associated with public procurement contracts accounts for losses of billions of euros every year. Thus, it is of utmost relevance to explore analytical frameworks that can help public authorities identify agents that are more susceptible to irregular activities. Here, we use standard network science methods to study the co-bidding relationships between firms that participate in public tenders issued by the 184 municipalities of the State of Ceará (Brazil) between 2015 and 2019. We identify 22 groups/communities of firms with similar patterns of procurement activity, defined by their geographic and activity scopes. The profiling of the communities allows us to highlight organizations that are more susceptible to market manipulation and irregular activities. Our work reinforces the potential application of network analysis in policy to unfold the complex nature of relationships between market agents in a scenario of scarce data.

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This paper analyses foreign direct investment (FDI) of Angola in Portugal. The reverse investment of African countries in Europe is a recent economic event that needs to be analysed, theoretically explained and empirically tested. A dynamic theoretical model is presented and a Bayesian model tests the model validating it. The results reveal that imports and corruption increase Angola FDI in Portugal. Some variables affect negatively Angola FDI in Portugal such as lagged Angola FDI, signifying an autoregressive negative effect in Portugal; the Portuguese official development assistance (ODA) to Angola, which are direct transfers from Portugal to Angola; and Angola's GDP. Policy implications are discussed.Ã The authors acknowledge financial support from national funds by FCT (Fundação para a Ciência e a Tecnologia). This article is part of the Strategic Project: PEst-OE/EGE/UI0436/2014.

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