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Good practice Imported

Sierra Leone's AI-Assisted Digital Asset Declaration System — Anti-Corruption Commission and DSTI Target Public-Official Compliance

Sierra Leone · Freetown · See the Sierra Leone profile · See the Freetown profile

Evidence: Observational / pre–post Top 74% 47/100 · Ask Evidence Copilot about this practice

Sierra Leone's Anti-Corruption Commission and Directorate of Science, Technology & Innovation built an AI-assisted digital platform for public officials to declare assets, with analytics meant to flag inconsistencies; over 17,000 officials complied in its first full cycle.

17,186 officials
Officials who declared assets, 2024/25 cycle (2024/25)
67 officials (0.39%)
Defaulters, 2024/25 cycle (2024/25)
13,036 of 16,144 targeted officers
Officials declared, 2026 cycle (as of 18 August 2026) (as of 18 August 2026)
Sierra Leone's AI-Assisted Digital Asset Declaration System — Anti-Corruption Commission and DSTI Target Public-Official Compliance

Details

Maturity
Scaling
Promoter
Sierra Leone Anti-Corruption Commission (ACC)
Period
2025–ongoing
Keywords
anti-corruption, public integrity, digital government, civil service

Context

Sierra Leone's Anti-Corruption Commission (ACC) and the Directorate of Science, Technology & Innovation (DSTI) jointly built a Digital Asset Declaration System, completed in June 2025, that lets public officials file mandatory asset declarations online and applies AI-driven analytics intended to flag inconsistencies and potential risks for further review.

Objectives

The system aims to digitise mandatory asset declaration for public officials while adding AI-driven analytics intended to flag inconsistencies and potential risks for further review by investigators.

Activities

The ACC and DSTI held public live demonstrations of the platform before each declaration cycle to walk officials through registration and filing.

Results

Participation has been strong: in the 2024/25 declaration cycle, 17,186 public officials declared their assets, leaving only 67 defaulters (0.39%); in the 2026 biennial exercise, 13,036 of 16,144 targeted officers had declared as of 18 August 2026.

Conclusions

What is not yet publicly documented is the performance of the AI analytics component itself — how many inconsistencies it has actually flagged, how many led to investigation, or how its outputs compare with manual review; the figures above measure participation and compliance, not the fraud-detection algorithm's accuracy, so its added value beyond a well-run digital filing system is still unproven.

Implementation
Indicative cost
Medium (€50k–€500k) — No public cost figures were found; conservatively banded medium for a national digital filing and analytics platform built jointly by ACC and DSTI.
Time to results
Medium (1–3 years) — Completed June 2025 with a first full cycle in 2024/25 and a second biennial cycle in 2026; banded medium given the two-cycle track record.
Staffing & skills
Anti-Corruption Commission (ACC) and Directorate of Science, Technology & Innovation (DSTI) joint delivery team

Conditions for success

  • Mandatory asset-declaration legal requirement for public officials already in place
  • Public live demonstrations before each cycle to build official familiarity with the platform

Common failure modes

  • The AI analytics component's own performance (flags raised, investigations triggered, accuracy) is not publicly documented, so its added value beyond digital filing is unproven
  • No independent audit of the anomaly-detection logic has been published

Where it fits

Governance type
independent anti-corruption commission paired with a technical government innovation directorate
Scale
national, covering the full population of legally required declarants (16,000+ officials)
Income level
low-income

Commonly funded by

National / regional programmes

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Data sources

Where this practice's information was retrieved from, and when.

Attachments

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