IDEAS home Printed from https://ideas.repec.org/a/bla/ijhplm/v40y2025i6p1341-1347.html

Artificial Intelligence and Corruption: Opportunities and Challenges in the Health Sector

Author

Listed:
  • Paula del Rey‐Puech
  • Dina Balabanova
  • Martin McKee

Abstract

Corruption in health systems diverts resources, erodes trust, and reduces service quality. Traditional oversight methods struggle to detect fraudulent patterns, but Artificial Intelligence (AI) offers new possibilities. AI can analyse large datasets to predict corruption risks and detect irregularities in procurement, insurance claims, and counterfeit medicines. Successful applications include AI‐powered tools that flag suspicious transactions, expose bid‐rigging in procurement, and identify fraudulent medical billing. AI can also complement other analytical tools to help track counterfeit drug supply chains through image recognition and network analysis. However, AI's impact depends on how it is deployed. Government‐led AI initiatives may enhance transparency but risk reinforcing power imbalances or enabling authoritarian control. In contrast, civil society‐driven efforts can empower citizens to hold authorities accountable but face challenges like limited data access and misinformation risks. Moreover, AI can also facilitate corruption in the health system through biased algorithms, deepfake propaganda, or manipulated AI‐driven decision‐making in resource allocation. Maximising AI's anti‐corruption potential in healthcare requires investments in skilled personnel and data systems. AI should complement human oversight, with transparent auditing mechanisms to mitigate biases. Integrating blockchain and AI technologies may enhance accountability by securing procurement records and preventing data manipulation. While AI presents significant opportunities, its application to anti‐corruption remains a political issue as much as a technological one. Careful governance, ethical and legal safeguards, and balanced implementation will determine whether AI combats corruption or exacerbates abuses.

Suggested Citation

  • Paula del Rey‐Puech & Dina Balabanova & Martin McKee, 2025. "Artificial Intelligence and Corruption: Opportunities and Challenges in the Health Sector," International Journal of Health Planning and Management, Wiley Blackwell, vol. 40(6), pages 1341-1347, November.
    • Handle: RePEc:bla:ijhplm:v:40:y:2025:i:6:p:1341-1347
    DOI: 10.1002/hpm.70002
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/hpm.70002
    Download Restriction: no
    File URL: https://libkey.io/10.1002/hpm.70002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Félix J. López-Iturriaga & Iván Pastor Sanz, 2018. "Predicting Public Corruption with Neural Networks: An Analysis of Spanish Provinces," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 140(3), pages 975-998, December.
    2. Gallego, Jorge & Rivero, Gonzalo & Martínez, Juan, 2021. "Preventing rather than punishing: An early warning model of malfeasance in public procurement," International Journal of Forecasting, Elsevier, vol. 37(1), pages 360-377.
    3. Henry Adobor & Robert Yawson, 2023. "The promise of artificial intelligence in combating public corruption in the emerging economies: A conceptual framework," Science and Public Policy, Oxford University Press, vol. 50(3), pages 355-370.
    4. Johannes Wachs & J'anos Kert'esz, 2019. "A network approach to cartel detection in public auction markets," Papers 1906.08667, arXiv.org.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Vitezslav Titl & Deni Mazrekaj & Fritz Schiltz, 2024. "Identifying Politically Connected Firms: A Machine Learning Approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(1), pages 137-155, February.
    2. Elliott Ash & Sergio Galletta & Tommaso Giommoni, 2025. "A Machine Learning Approach to Analyze and Support Anticorruption Policy," American Economic Journal: Economic Policy, American Economic Association, vol. 17(2), pages 162-193, May.
    3. Guido de Blasio & Alessio D'Ignazio & Marco Letta, 2020. "Predicting Corruption Crimes with Machine Learning. A Study for the Italian Municipalities," Working Papers 16/20, Sapienza University of Rome, DISS.
    4. de Blasio, Guido & D'Ignazio, Alessio & Letta, Marco, 2022. "Gotham city. Predicting ‘corrupted’ municipalities with machine learning," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    5. Jose Joaquin Pozo-Antúnez & Horacio Molina-Sánchez & Antonio Ariza-Montes & Francisco Fernández-Navarro, 2021. "Promoting work Engagement in the Accounting Profession: a Machine Learning Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 157(2), pages 653-670, September.
    6. Sirui Nie & Yun Chen & Jingjing Li & Wenxi Zhu & Chongsen Ma, 2022. "Analysis on the Types and Characteristics of Collusion Behavior of Government Investment Project Tenderees," Complexity, John Wiley & Sons, vol. 2022(1).
    7. Mihály Fazekas & Johannes Wachs, 2020. "Corruption and the Network Structure of Public Contracting Markets across Government Change," Politics and Governance, Cogitatio Press, vol. 8(2), pages 153-166.
    8. Bedri Kamil Onur Tas, 2024. "A machine learning approach to detect collusion in public procurement with limited information," Journal of Computational Social Science, Springer, vol. 7(2), pages 1913-1935, October.
    9. J'anos Kert'esz & Johannes Wachs, 2020. "Complexity science approach to economic crime," Papers 2008.12364, arXiv.org.
    10. Gallego, Jorge & Prem, Mounu & Vargas, Juan F., 2020. "Corruption in the Times of Pandemia," SocArXiv js8by, Center for Open Science.
    11. Barone, Guglielmo & Letta, Marco, 2025. "Unlevel playing field? Machine learning meets state aid regulation," International Journal of Industrial Organization, Elsevier, vol. 101(C).
    12. Gallego, Jorge & Rivero, Gonzalo & Martínez, Juan, 2021. "Preventing rather than punishing: An early warning model of malfeasance in public procurement," International Journal of Forecasting, Elsevier, vol. 37(1), pages 360-377.
    13. Michela Gnaldi & Simone Del Sarto, 2024. "Measuring Corruption Risk in Public Procurement over Emergency Periods," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 172(3), pages 859-877, April.
    14. Kurdoglu, Berkay & Yucel, Eray, 2022. "A Cointegration-based cartel screen for detecting collusion," MPRA Paper 113888, University Library of Munich, Germany.
    15. De Leverano, Adriano, 2023. "Clustered bids in first-price auctions: Collusion or competition?," Economics Letters, Elsevier, vol. 233(C).
    16. Granlund, David & Rudholm, Niklas, 2023. "Calculating the probability of collusion based on observed price patterns," Umeå Economic Studies 1014, Umeå University, Department of Economics, revised 13 Oct 2023.
    17. Belev, S. & Veterinarov, V. & Matveev, E., 2023. "Vertical collusion in public procurement: Estimation based on data for R&D composite auctions," Journal of the New Economic Association, New Economic Association, vol. 59(2), pages 36-63.
    18. Del Sarto, Simone & Gnaldi, Michela & Salvini, Niccolò, 2024. "Sustainability and high-level corruption in healthcare procurement: Profiles of Italian contracting authorities," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
    19. Gabrielli, M. Florencia & Willington, Manuel, 2023. "Estimating damages from bidding rings in first-price auctions," Economic Modelling, Elsevier, vol. 126(C).
    20. Andreas Bovin & Iwan Bos, 2026. "Market Shares as a Collusive Marker: Evidence From the European Truck Industry," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 35(2), pages 250-265, April.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:ijhplm:v:40:y:2025:i:6:p:1341-1347. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0749-6753 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.