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Artificial Intelligence Techniques to Detect and Prevent Corruption in Procurement: A Systematic Literature Review

Abstract:

Transparency International estimates that the costs of corruption in public procurement reach between 20 and 25% of the contract value, sometimes reaching 40–50%. In this study, we analyzed differentness kinds of corruption like (bribery, collusion embezzlement, misappropriation, fraud, abuse of discretion, favoritism, nepotism), and six types of Artificial Intelligence techniques (classification, regression, clustering, pbkp_rediction, outlier detection, and visualization). The methodology proposed by Torres-Carrion was used, and four research questions were raised, which allow knowing the types of research carried out, the characteristics of the organizations in which the investigations are carried out, the technological tools, and data mining methodologies and techniques. The search was done in the Scopus and Web of Science databases, getting 102 articles published between 2015 and 2019. The primary data mining techniques used are logistic models, neural networks, Bayesian networks, supported vector machines, and decision trees.