The world is changing and so is the way it is measured. For decades, policymakers and the private sector have relied on data released by official statistical institutions to assess the state of the economy.

With an esteemed line-up of international speakers, drawn from across Europe, Asia and the Middle East, the forum will inform and provide insight to all participants during the one-day programme.

Deep learning – an advanced form of artificial intelligence – can be more accurate in predicting outcomes, compared with conventional econometric approaches, according to research from Bank of Korea (BoK). The research paper tested predictions of monthly exports from Korea and daily Korean won-US dollar exchange rates. It found that deep learning approaches produced better results even with the sorts of non-granular data sets that are normally used for conventional econometric models.

Artificial intelligence (AI) is increasingly useful for central banks. While it may be used only in low-level roles today, technological advances and cost savings will likely embed AI deeper and deeper into core central bank functions. Maybe each central bank will have their own AI engine, maybe a future'BoB' (the Bank of England Bot). What will be the impact of BoB and its counterparts? BoB could today, or soon, help with many central bank tasks, such as information gathering, data analysis, forecasting, risk management, financial supervision, and monetary policy analysis. The technology is mostly here; what prevents adoption are cultural, political, and legal factors.

The use of machine learning (ML) can produce forecasts that are more accurate than standard statistical methods, research published by the Reserve Bank of India finds. They focus on forecasting CPI inflation and its components.

The concept of big data can be hard to pin down – how would you define it? Per Nymand-Andersen: Big data can be defined as a source of information and intelligence resulting from the recording of operations, or from the combination of such records. There are many examples of recorded operations – records of supermarket purchases, robot and sensor information in production processes, satellite sensors, images, as well as behaviour, event and opinion-driven records from search engines, including information from social media and speech recognition tools. The list seems endless, with more and more information becoming public and digital as a result – for example, the use of credit and debit payments, trading and settlement platforms, and housing, health, education and work-related records. Should central banks take advantage of big data?