Detecting Bid-Rigging in Public Procurement. A Cluster Analysis Approach

Bușu, Mihail; Buṣu, Cristian

doi:10.3390/admsci11010013

Cited by 6 publications

(3 citation statements)

References 32 publications

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“…For this, they use techniques such as association rules and random forest. Articles such as [2], [18], and [26] use clustering algorithms to group competitive and non-competitive bidding processes based on data such as the ratio between the bid values offered by companies and initial value of the contract.…”

Section: Resultsmentioning

confidence: 99%

“…Some works, such as [7], [16] and [21], seek to identify potential fraud signs in terms of the bid opening. Other works, such as [2] and [18], use variables found during the bidding process to find collusions, such as bid values and time intervals. Finally, works such as [22] and [4] based on the compilation of different bidding processes already carried out in search of participation patterns and winners.…”

Section: Qp1: What Crime Types Against Public Administration Are Most...mentioning

confidence: 99%

“…Once found, they apply logistic regression or deep learning algorithms to detect a competitiveness lack in bidding terms, which could point to a possible collusion between the bidder and interested companies. On the other hand, works aimed at identifying fraud in the same processes using data generated during the bidding process, such as [2], [26] and [18], using data as the ratio between bid value and initial bid value, through clustering algorithms to differentiate competitive and non-competitive processes. Finally, clustering algorithms, as well as association rules used by works such as [22] and [4] to identify collusions between companies and suppliers through several bidding process analysis.…”

Section: Qp1: What Crime Types Against Public Administration Are Most...mentioning

confidence: 99%

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Data science to identify crimes against public administration

Souza,

Júnior,

Fontes

et al. 2023

Annals of Computer Science and Information Systems

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Context:The management of public resources is subject to illegal acts and the automatic identification of such acts depends on the analysis of a lot of data. Objective: The object of this work is the analysis of scientific publications through a study based on systematic mapping with the purpose of evaluating them in relation to the use of automated tools to identify crimes against public administration in databases from the perspective of researchers in the data science context. Method: Using PICO strategy (Population, In-tervention, Comparison, and Outcome), a systematic mapping was conducted to find the primary studies in the literature and collect evidence for directing future research. Results: Nineteen works were found that fit the proposed cri-teria. Almost 80% of the studies found seek to identify some type of fraud in bidding processes, obtaining accuracies between 72% and 99%. The research also revealed different techniques for approaching the problem. Considering all the works, the most used databases are bidding bases, lawsuits, public notices and corporate structure of companies, respectively. Conclusions: The work has shown a recent increase in interest in analyzing public data for irregularities. It is expected that this analysis will help control bodies elucidating different ways of detecting crimes against the public administration in an automated way.

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Section: Resultsmentioning

confidence: 99%

Section: Qp1: What Crime Types Against Public Administration Are Most...mentioning

confidence: 99%

Section: Qp1: What Crime Types Against Public Administration Are Most...mentioning

confidence: 99%

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Data science to identify crimes against public administration

Souza,

Júnior,

Fontes

et al. 2023

Annals of Computer Science and Information Systems

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Bid-rigging in public procurement: cartel strategies and bidding patterns

Carbone,

Calderoni,

Jofre

2024

Crime Law Soc Change

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Bid-rigging harms economies and societies. While existing research has primarily focused on quantifying the economic damages resulting from bid-rigging cartels, there is a relative dearth of studies exploring how firms interact and the specific techniques they use to rig tenders. Our paper examines the bidding behaviours associated with bid-rigging. Specifically, we investigate how cartel companies exploit legal opportunities, engage in joint and similar bidding and adapt tactics based on the number of colluding bidders. Our study relies on judicial evidence and a dataset of 1,242 companies (including 112 colluding entities) participating in 357 roadwork bid auctions in Italy. Through bootstrap logistic regressions, we analyse company-level indicators and their association with cartel involvement. The results reveal that cartels frequently exploit subcontracts and price similarity. Moreover, we find that bid-rigging tactics vary depending on the number of bidding cartel companies involved. When colluding companies are the majority of bidders, cartels rely on widespread member participation to cover a broad range of prices. Conversely, when cartel companies constitute less than half of the bidders, they tend to form temporary associations. These findings untangle the complexity inherent in cartel agreements and strategies, highlighting the importance of assessing firm interactions and relational patterns within co-bidding networks for a comprehensive understanding of collusive dynamics.

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