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Weak Supervision Can Help Detecting Corruption in Public Procurement

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Intelligent Systems and Applications (IntelliSys 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 544))

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Abstract

Corruption in public procurement in the European Union is estimated to cost more than 120 billion Euros per year. Detecting corruption in public procurement is challenging because of imperfect corruption reg flags and vast number of tenders. Authorities need potent and scalable tools to identify corrupt public procurement practices. This paper shows that weak supervision machine learning algorithm provides a proficient method that can easily be implemented. Authorities can effectively calculate corruption probabilities of millions of public procurement tenders using the weak supervision algorithm. Weak supervision combines information contents of imperfect corruption red flags with unknown accuracies. Additionally, it can handle measurement errors. I analyze potential corruption in 25,859,734 European Union (EU) public procurement contracts in years 2009–2020 using the Snorkel weak supervision algorithm. These contracts are awarded by 1,212,533 authorities in 33 EU and affiliated countries. The analysis suggests that 40% of contracts and 22% of EU authorities are susceptible to corruption. Experimental results show that training machine learning models with weak supervision labeled data produces superior prediction results.

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Notes

  1. 1.

    Calculated using data from digiwhist.eu.

  2. 2.

    Calculated using data from usaspending.gov.

  3. 3.

    The Common Procurement Vocabulary (CPV) establishes a single classification system for public procurement aimed at standardizing the references used by contracting authorities and entities to describe the subject of procurement contracts.

    See https://simap.ted.europa.eu/web/simap/cpv.

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Correspondence to Bedri Kamil Onur Tas .

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Tas, B.K.O. (2023). Weak Supervision Can Help Detecting Corruption in Public Procurement. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2022. Lecture Notes in Networks and Systems, vol 544. Springer, Cham. https://doi.org/10.1007/978-3-031-16075-2_40

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