Project Neo: gaining new economic insights with AI and novel data sources

21 March 2024

The Bank for International Settlements (BIS) is launching Project Neo to explore how more frequent and granular data from novel sources combined with advanced analytic technologies can help central banks better fulfil their mandates.

A joint effort by the BIS Innovation Hub Swiss Centre and the Swiss National Bank (SNB), Project Neo aims to ensure that central bank policymaking decisions are grounded in timely and precise intelligence. 

Central banks are actively examining how advanced data analytics (such as artificial intelligence ) can strengthen the effectiveness of their policy work. Project Neo is the first in a series of experiments that the BIS Innovation Hub plans to undertake to understand how technology can help central banks in their core functions. The Innovation Hub is therefore expanding the scope of one of its six core themes to include "monetary policy tech" together with "suptech and regtech". These are the use of technology for supervision and oversight and for meeting regulatory and compliance requirements, respectively.

Project scope

Official macroeconomic statistics, such as gross domestic product and inflation, often suffer from delays and a lack of detail, complicating effective decision-making. To address this, Project Neo aims to: 

  • explore novel data sources from both private companies and public institutions, such as air transport, road traffic, air pollution, electricity, cargo transport and retailers and payment services; and
  • use advanced data science techniques to gain new economic insights by creating unique economic indicators and produce relevant forecasts of macroeconomic statistics.

Project Neo is innovative in that it aims to work with unique data from national private companies. It will combine this information with typical macro-financial data sets, targeting a broad range of official statistics. At the same time, Project Neo will quantify the relevance of using disaggregated micro data to understand and forecast macroeconomic indicators. The project will benefit from exploring the synergy between economists and data engineers and use machine learning and predictive modelling.

The project will initially work with data from Switzerland, but the prototype and learning experience will be applicable to any other country.