Red flags are widely used to minimize the risk of various forms of economic misconduct, among which corruption in public procurement. Drawing on criminal investigations, the literature has developed several indicators of corruption in public procurements and has put them forward as viable risk indicators. But are they genuinely viable, if only corrupt procurements are analysed? Using a dataset of 192 public procurementswith 96 cases where corruption was detected and 96 cases where corruption was not detectedthis paper addresses the identification of significant risk indicators of corruption. We find that only some indicators significantly relate to corruption and that eight of them (e.g. large tenders, lack of transparency and collusion of bidders) can best predict the occurrence of corruption in public procurements. With this paper we successfully tap into one of the most vulnerable areas of criminological researchselecting the right sampleand consequently, our results can help increase the detection of corruption, increase investigation effectiveness and minimize corruption opportunities.

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Indicators of compliance and efficiency in combatting money laundering, collected by EUROSTAT, are plagued with shortcomings. In this paper, I have carried out a forensic analysis on a 2003–2010 dataset of indicators of compliance and efficiency in combatting money laundering, that European Union member states self-reported to EUROSTAT, and on the basis of which, their efforts were evaluated. I used Benford’s law to detect any anomalous statistical patterns and found that statistical anomalies were also consistent with strategic manipulation. According to Benford’s law, if we pick a random sample of numbers representing natural processes, and look at the distribution of the first digits of these numbers, we see that, contrary to popular belief, digit 1 occurs most often, then digit 2, and so on, with digit 9 occurring in less than 5% of the sample. Without prior knowledge of Benford’s law, since people are not intuitively good at creating truly random numbers, deviations thereof can capture strategic alterations. In order to eliminate other sources of deviation, I have compared deviations in situations where incentives and opportunities for manipulation existed and in situations where they did not. While my results are not a conclusive proof of strategic manipulation, they signal that countries that faced incentives and opportunities to misinform the international community about their efforts to combat money laundering may have manipulated these indicators. Finally, my analysis points to the high potential for disruption that the manipulation of national statistics has, and calls for the acknowledgment that strategic manipulation can be an unintended consequence of the international community’s pressure on countries to put combatting money laundering on the top of their national agenda.

Financial and legal entities (e.g. banks, casinos, notaries etc.) have to report money laundering suspicions. Countries’ engagement in fighting money laundering is evaluated–among others–with statistics on how often these suspicions are reported. Lack of compliance can result in economically harmful blacklisting. Nevertheless, these blacklists repeatedly become empty–in what is known as the emptying blacklist paradox. We develop a principal-agent model with intermediate agents and show that non-harmonized statistics can lead to strategic reporting to avoid blacklisting, and explain the emptying blacklist paradox. We recommend the harmonization of the standards to report suspicion of money laundering.

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