evidoria

← Back to browse

Good practice

DOZORRO — Ukraine's AI Risk-Flagging System for Public Procurement

Ukraine · Kyiv · See the Ukraine profile

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)
3,500 tenders assessed by 20 procurement experts
Tenders used to train the risk model
165
Procurement procedures analysed (July 2025)
10 billion UAH
Value of procedures analysed (July 2025)
89 of 165
Procedures with violations found (July 2025)
133 million UAH
Estimated inefficient spending prevented (H1 2025)
DOZORRO — Ukraine's AI Risk-Flagging System for Public Procurement

Details

Maturity
Established
Promoter
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.

Attachments

Similar practices you may find useful