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Good practice Imported

Publican AI — Ghana’s machine-learning customs enforcement at Tema Port

Ghana · Accra · See the Ghana profile · See the Accra profile

Evidence: Descriptive / self-reported Top 93% 27/100 · Ask Evidence Copilot about this practice

Ghana’s GRA deployed Publican AI at Tema Port in February 2026 to screen import declarations. Monthly customs revenue rose from GH₵2.4 bn to GH₵3.6 bn within weeks; early claims of USD 3 M/day additional revenue are disputed by independent analysts and a formal audit is pending.

2.4 GH₵ billion
Monthly customs revenue before deployment (pre-deployment baseline)
3.6 GH₵ billion
Monthly customs revenue after deployment (first weeks after 1 Feb 2026 deployment)
3 USD million/day
Claimed additional revenue generated by the system (as of April 2026)
25 %
Import declarations flagged for additional scrutiny
from ~2 hours to ~5 minutes
Manual processing time per declaration
11 GH₵ billion/year
Estimated annual revenue historically lost to valuation fraud and under-invoicing (2020–2024)
Publican AI — Ghana’s machine-learning customs enforcement at Tema Port

Details

Maturity
Pilot
Promoter
Ghana Revenue Authority (GRA)
Period
2026–present
Keywords
customs enforcement, import valuation, machine learning, anomaly detection, trade compliance

Context

The Ghana Revenue Authority (GRA) deployed Publican AI at Tema Port, Ghana's primary import gateway, from 1 February 2026. The system uses machine-learning models to analyse import declarations and benchmark stated values against global trade price databases.

Objectives

Publican AI is presented by GRA as decision-support technology: it assigns a risk score to each import declaration and flags anomalies for human review before shipment clearance, aiming to reduce valuation fraud and under-invoicing and speed up processing.

Activities

The system flags roughly 25% of declarations for additional scrutiny and is reported to have cut manual processing time from around two hours to about five minutes per declaration. GRA cites historical estimates of GH₵11 billion in annual revenue lost to valuation fraud and under-invoicing over 2020–2024 as the problem the system targets.

Results

In its first weeks, GRA data showed monthly customs revenue rising from GH₵2.4 billion to GH₵3.6 billion, a near-50% increase attributed largely by GRA to improved import valuation. By April 2026, GRA claimed the system was generating roughly USD 3 million per day in additional revenue.

Conclusions

Independent analysts have publicly questioned whether the revenue surge is causally attributable to the AI system rather than other enforcement factors, calling the evidence too early to verify and requesting a formal independent audit, which had not been completed as of June 2026. Traders report they cannot understand how customs values are determined, disputes are escalated to a secretariat meeting only twice weekly, and no algorithmic transparency has been published. The Ministry of Finance convened a multi-party technical committee in April 2026 in response. This is an early-stage deployment with promising but contested results and documented governance gaps, not yet a verified success.

Implementation

Indicative cost
Medium (€50k–€500k)
Time to results
Short (< 1 year)
Staffing & skills
Ghana Revenue Authority (GRA) customs and IT staff operating and reviewing flagged declarations, Multi-party technical committee convened by the Ministry of Finance (from April 2026) to oversee the system

Conditions for success

  • A completed, independent audit verifying the causal link between the system and the revenue increase
  • Published algorithmic transparency on how customs values are determined
  • A dispute-resolution mechanism responsive enough to match the volume of contested valuations

Common failure modes

  • Revenue increase attributed to the system by GRA but publicly contested by independent analysts as unverified
  • Traders report they cannot understand how customs values are determined
  • Dispute secretariat meets only twice weekly, creating a bottleneck for contested cases
  • No published algorithmic transparency and no completed independent audit as of June 2026

Commonly funded by

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Data sources

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

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