Abstract
Public procurement fraud is a plague that produces significant economic losses in any state and society, but empirical studies to detect it in this area are still scarce. This article presents a review of the most recent literature on public procurement to identify techniques for fraud detection using Network Science. Applying the PRISMA methodology and using the Scopus and Web of Science repositories, we selected scientific articles and compared their results over a period from 2011 to 2021. Employing a compiled search string, we found cluster analysis and centrality measures as the most adopted techniques.
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References
Carneiro, D., Veloso, P., Ventura, A., Palumbo, G., Costa, J.: Network analysis for fraud detection in portuguese public procurement. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds.) IDEAL 2020. LNCS, vol. 12490, pp. 390–401. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-62365-4_37
Cheng, T., Liu, T., Meng, L., et al.: The analysis of water project bid rigging behavior based on complex network. In: International Conference on Applied Mathematics, Modeling and Simulation (AMMS) (2017)
Costa, G.A., Machado, D.P., Martins, V.Q.: The efficiency of social control in municipal bidding: a study in social observatories. Sociedade Contabilidade e Gestão 14(4), 112 (2020)
Davydenko, V.I., Morozov, N.V., Burmistrov, M.I. Adaptation of cluster analysis methods in respect to vector space of social network analysis indicators for revealing suspicious government contracts. In: IEEE 5th International Conference on Future Internet of Things and Cloud (FiCloud) (2017)
Fazekas, M., Tóth, I.J.: From corruption to state capture: a new analytical framework with empirical applications from Hungary. Polit. Res. q. 69(2), 320–334 (2016)
Fazekas, M., Wachs, J.: Corruption and the network structure of public contracting markets across government change. Politics and Governance (2020)
Grassi, R., Calderoni, F., Bianchi, M., Torriero, A.: Betweenness to assess leaders in criminal networks: new evidence using the dual projection approach. Soc. Networks 56, 23–32 (2019)
Hosseini, M.R., Martek, I., Banihashemi, S., et al: Distinguishing Characteristics of Corruption Risks in Iranian Construction Projects: A Weighted Correlation Network Analysis. Science and Engineering Ethics (2019)
Lei, M., Yin, Z., Li, S., Li, H.: Detecting the collusive bidding behavior in below average bid auction. In: 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) (2017)
Lin, J., Khomnotai, L.: Improving fraudster detection in online auctions by using neighbor-driven attributes. Entropy, Vol. 18, Ed:1, N:e18010011 (2016)
Lin, S.J., Jheng, Yi-Y., Yu, C.H.: Combining ranking concept and social network analysis to detect collusive groups in online auctions. Expert Syst. With Applicat (2012)
Luna-Pla, I., Carlock. N.J.R.: Corruption and complexity: a scientific framework for the analysis of corruption networks. Appl. Network Sci. (2020)
Marsden, P.V.: Network Analysis. In: Encyclopedia of Social Measurement (2005)
Morselli, C.: Inside Criminal Networks. Springer, Studies of Organized Crime (2008)
Mufutau, G.O., Mojisola. O.V.: Detection and prevention of contract and procurement, fraud Catalyst to organization profitability. J. Bus. Manag. (2016)
Padhi, S.S., Mohapatra, P.K.J.: Detection of collusion in government procurement auctions. J. Purch. Supply Manag. 17, 207–221 (2011)
Page, M.J., McKenzie, J.E., Bossuyt, P.M., et al.: The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Int. J. Surg. 88 (2021). Number 105906
Reeves-Latour, M., Morselli, C.: Bid-rigging networks and state corporate crime in the construction industry. Soc. Networks 51, 158–170 (2017)
Rustiarini, N., Sutrisno, T., Nurkholis, N., Andayani, W.: Why people commit public procurement fraud? the fraud diamond view. J. Pub. Procur. 19(4), 345–362 (2019)
Sedita, S.R., Apa, R.: The impact of inter-organizational relationships on contractors’ success in winning public procurement projects: The case of the construction industry in the Veneto region. Int. J. Proj. Manag. (2015)
Silva Filho, J.B.: A eficiência do controle social nas licitações e contratos administrativos. Master's thesis - Universidade Nove de Julho, São Paulo (2017)
Van Eck N.J., Waltman L.: Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–38. Version 1.6.14 (2010)
Wachs, J., Fazekas, M. and Kertész, J.: Corruption risk in contracting markets: a network science perspective. Internat. J. Data Sci. Analyt. (2021)
Wachs, J., Kertesz, J. (2019b). A network approach to cartel detection in public auction markets. Sci. Rep.
Wachs, J., Yasseri, T., Lengyel, B., Kertesz, J. (2019a). Social capital predicts corruption risk in towns. Royal Society Open Science.
Wensink, W., Vet, M.J. (2013). Identifying and Reducing Corruption in Public Procurement in the EU. European Commission. Bruxelles.
Whiteman, R. (2019). Fraud and corruption tracker. The Chartered Institute of Public Finance and Accountancy – CIPFA.
World Bank Group: A fair adjustment: efficiency and equity of public spending in Brazil. Volume 1 - Overview (English). Washington, D.C. (2017)
Zhu, J, Wang, B., Li, L., et al.: Bidder network community division and collusion suspicion analysis in Chinese construction projects. Adv. Civil Eng. (2020)
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This research was funded by “Fundação para a Ciência e a Tecnologia” (Portugal), grants’ number DSAIPA/DS/0116/2019
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Lyra, M.S., Pinheiro, F.L., Bacao, F. (2022). Public Procurement Fraud Detection: A Review Using Network Analysis. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications X. COMPLEX NETWORKS 2021. Studies in Computational Intelligence, vol 1072. Springer, Cham. https://doi.org/10.1007/978-3-030-93409-5_11
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