DOZORRO's ML risk-flagging tool screens Ukraine's Prozorro procurement tenders for corruption red flags, catching 26% more unfair-selection and 298% more collusion cases than fixed indicators in testing, and helped prevent UAH 133m in wasteful spending in H1 2025.
+26%
Beta-test unfounded-winner-selection flags vs fixed-indicator system (2018 beta)
+37%
Beta-test groundless-disqualification flags vs fixed-indicator system (2018 beta)
+298%
Beta-test collusion-case flags vs fixed-indicator system (2018 beta)
DOZORRO civic-monitoring coalition (led by Transparency International Ukraine), with Ukraine's Ministry of Economy, integrated with Prozorro
Period
Model trained from July 2018; operational since Q1 2019
Keywords
public procurement, anti-corruption, compliance, AI
Context
Prozorro is Ukraine's mandatory electronic public-procurement system; DOZORRO is the civic-monitoring layer built on top of it by a coalition led by Transparency International Ukraine, in cooperation with the Ministry of Economy.
Objectives
Use machine learning, trained on how procurement experts assessed tenders, to flag corruption-risk patterns that a fixed checklist of indicators could be learned and circumvented by officials.
Activities
From July 2018, developers trained the model on roughly 3,500 tenders independently assessed by 20 procurement experts. The tool went into beta in November 2018 and full operation in Q1 2019. High-risk tenders identified by the model are routed to civil-society monitors, who decide whether to escalate to auditors or law enforcement.
Results
In beta testing the model flagged 26% more tenders with unfounded winner selection, 37% more with groundless disqualification, and 298% more apparent collusion cases than the State Audit Service's separate system of 35 fixed risk indicators. In July 2025 alone, monitors analysed 165 procedures worth over UAH 10 billion and found violations in 89 of them; across H1 2025 the coalition's interventions helped prevent an estimated UAH 133 million in inefficient spending.
Conclusions
The underlying model's exact features and false-positive rate are not publicly documented, limiting independent verification of the beta-test percentages beyond the developers' own account.
Implementation
Indicative cost
Medium (€50k–€500k)
Time to results
Medium (1–3 years)
Staffing & skills
Procurement experts labelling training tenders, ML developers, Civil-society monitors reviewing flagged tenders
Conditions for success
A mandatory, centralised e-procurement system (Prozorro) already generating structured tender data to train on
A defined escalation pathway from flagged tenders to auditors and law enforcement
A named civil-society coalition plus the Ministry of Economy jointly operating the system
Common failure modes
Model features and false-positive rate are not published, limiting independent verification of the reported catch-rate improvements
Data sources
Where this practice's information was retrieved from, and when.
Romania's ANI automatically cross-references procurement officials' identities with registered bidders before contracts are signed; over 100,000 procedures worth RON 72 …