Administrative Health Research (AHR)

An Approach to Proactive Identification and Algorithmic Analysis of Corruption Grounds in Government Contracts: Transparency Beyond Reactive Monitoring with Machine Learning

Articles in Press

Document Type : Original Article

Author

Seyed mohamad Etemad ejazi

General Inspection Office of Ardabil Province

Abstract
This research adopts a descriptive-analytical approach to examine the challenges of economic corruption in Iran's government contracts and proposes a novel solution based on machine learning for identifying corruption-prone anomalies. By analyzing documents and supervisory reports from 2002 to 2021, structural deficiencies in the public procurement system were identified, including ambiguity in legal jurisdictions, lack of integrated systems, extensive managerial discretion, and budgetary non-transparency. The Random Forest model has been introduced as a non-parametric tool for analyzing complex patterns in relationships between stakeholders (such as sham tenders or abnormal concentration of resources). Findings indicate that leveraging machine learning and real-world data from systems like "Setad" leads to the development of an advanced system for monitoring government contracts. This system not only tracks violations (like unusual collaborations) but also enables preemptive intervention by detecting early warning indicators. Despite data limitations, this approach can significantly enhance transparency and accountability in the utilization of Iran's public resources.

Keywords

Transparency
Government Contracts
Machine Learning
Economic Corruption
Hyper-Forest
Subjects
Administrative Health Research (AHR)

Articles in Press, Accepted Manuscript
Available Online from 11 July 2026

  • Receive Date 03 December 2025
  • Revise Date 24 May 2026
  • Accept Date 25 May 2026

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