e-Inflation — the Bank of Ghana's In-House AI and Big-Data Nowcasting System for Monetary Policy

In February 2025, Bank of Ghana Governor Dr Johnson Pandit Asiama directed the bank to take a "more proactive and precise approach to managing inflation, leveraging advanced data analytics and artificial intelligence." Eighteen months later, at the 4th Annual Statistics and Data Science Conference in Tamale (27 August 2026), First Deputy Governor Dr Zakari Mumuni detailed the result: an in-house electronic inflation-nowcasting methodology, e-Inflation, built to narrow the lag between real economic developments and the moment policymakers become aware of them.

e-Inflation sits alongside the Bank's existing Forecast and Policy Analysis System and Quarterly Projection Model. Machine-learning models complement the standard econometric GDP forecasts, and text-mining analytics extract signals from large volumes of unstructured information. On the supervisory side, the same push lets examiners validate granular bank data as it arrives rather than waiting on static monthly spreadsheets, surfacing emerging risks earlier.

Multiple Ghanaian outlets (Ecofin Agency, MyJoyOnline, B&FT, GhanaWeb, Norvan Reports) independently reported the same on-the-record briefing, and Dr Mumuni said the tools have "helped the Bank improve the accuracy of its inflation predictions, including forecasts made ahead of official data releases." No independently audited accuracy figures, such as mean-absolute-error reductions, have been published, and Mumuni himself cautioned that "technology can strengthen our intelligence, but it does not remove the need for human judgment" — a rare on-record acknowledgment of the tool's limits from the institution deploying it.

Read the full analysis: https://norvanreports.com/bank-of-ghana-turns-to-ai-big-data-and-machine-learning-to-sharpen-economic-forecasting/