ARTICLE
31 August 2026

Research on Risk Identification and Audit Governance of Bid Rigging and Collusion in Overseas Procurement Bidding: Joint Analysis Based on SDTM Model and Correlation Coefficient

Mingxuan Hou1
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1 CNPC Overseas Audit Center, Beijing 100000, China
PBES 2026 , 9(8), 251–257; https://doi.org/10.18063/PBES.v9i8.15166
© 2026 by the Author(s). Licensee Whioce Publishing, Singapore. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
Abstract

With the continuous expansion of central SOEs’ overseas business scale, overseas procurement bidding activities have formed a more complex and concealed risk structure. Bid rigging and collusion have evolved from traditional explicit collaborative behaviors into complex implicit and structured coordination across multiple entities. Against the backdrop of divergent cross-jurisdictional supervision and information asymmetry, experience-based audit approaches can no longer meet risk identification demands. On the basis of systematically analyzing the formation mechanism of bid rigging, this paper constructs a joint identification framework integrating the SDTM standard deviation model and correlation coefficient model to quantitatively identify bid rigging from two dimensions: quotation dispersion and coordination. The model is verified through a typical overseas information technology project. The research results demonstrate that the model can effectively detect abnormal quotation structures and potential collusive behaviors, providing methodological support for the audit governance of central SOEs’ overseas procurement.

Keywords
Bid rigging risk
SDTM model
Correlation coefficient
Audit governance
Overseas procurement
References

[1] Chen G, Wang C, 2025, Research on Procurement Auditing Based on Big Data. China Internal Audit, (02): 5360.

[2] Liu L, Li L, Xu M, 2023, Research on Intelligent Auditing Methods. China Internal Audit, (08): 1823.

[3] Cheng P, 2023, Research on the Transformation of Internal Auditing in the Big Data Environment. Friend of Accounting, (15): 148154.

[4] Yang Y, 2020, A Study on the Relationship Between Artificial Intelligence and Audit Quality. Technology and Economics, 39(05): 917–934.

[5] Bolton P, Dewatripont M, 2005, Contract Theory, MIT Press.

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