Big Data and Central Banks.

Advanced Project
3 min readMar 11, 2021


Today, we’ll talk about big data.

The world is changing and so is the way it is measured. For decades, policy makers and the private sector have relied on data released by official statistical institutions to assess the state of the economy. Collecting these data require substantial effort and publication often happens with a lag of several months or even years. However, in the past few years we have seen explosive growth in the amount of readily available data. New models of data collection and dissemination enabled the analysis of vast troves of data in real-time.

We’re living in the age of big data.

Big data and machine learning are now being used in almost every sector of the economy. Central banks are also increasingly using big data for research purposes and to inform policy decisions. In 2020, over 80% of central bank reports that they use big data, compare to when it was just 30% five years ago. Among the institutions that currently use big data, over 70% use it for economic research, while 40% state that they use it to inform policy decisions.

The most-frequent terms mentioned by central banks (Source: IFC 2020)

These numbers suggest that big data and machine learning offer many useful applications and can help central banks in fulfilling their mandate. Nowcasting GDP and inflation or examining spending patterns across regions and population subgroups in real time provide just two of many examples.

Yet, central banks also face challenges to unlock the full potential of big data and machine learning. A key topic of discussion is the availability of big data and tools to process, store and analyze. The design of legal frameworks or aspects of cyber security are also at the center of central bankers’ concerns. More practical problems are budget constraints and the difficulty in training existing or hiring new staff to work on big data related issues.

The IFC survey shows that central banks are willing to join forces to reap the benefits of big data. Indeed, half of them reported interest in collaborating in one or more specific projects.

The AUC project uses machine learning technology to credit the big data generated by the tier payment app to the underbanked people. This is one of our long-term projects.

By doing so, those who have not been able to use the bank could experience the advantages of the legacy financial system, and existing banks have the opportunity to increase additional revenue with the influx of new users.

The AUC team plans to enter the global market for Tier apps through strategic partnerships with traditional banks in each country, and tier apps are currently under development with the goal of launching in May 2021.

So please keep an eye on us.

Thanks to big data, machines can now be programmed to do the next thing right. But only humans can do the next right thing.

- Dov Seidman, CEO of LRN