Data Science for Central Banks and Supervisors: How to Make It Work, Actually

Public authorities, such as central banks and supervisory authorities, are not known for their ability to quickly adopt new techniques in a rapidly changing world. However, these authorities play a central role in society, such as safeguarding the financial system. The challenge of keeping the financial system safe is formidable and data science could potentially help. We discuss how to leverage the potential of data science using our experience at one of these organizations: DNB, the Dutch central bank. The dual role of DNB as central bank and prudential supervisor ensures that the lessons learned are of interest to all stakeholders in the public and financial sector. Furthermore, by adopting a strategy that prioritizes cloud-first and effectively establishing a Data Science Hub (DSH), the knowledge gained will have wider applicability.The goal of our study is two-fold. First, we demonstrate the significant potential of data science in nine lessons, all supported by our own projects.1 Based on our experience, we highlight the aspects necessary for fruitful data science work. It is a common misconception that getting data science to work for an organization can be achieved by hiring a few smart data geeks and having them develop “AI” in a remote corner of the organization. We will argue that AI should become part of daily work processes to reap the full benefits. Second, we share how we work at the DSH with the intent of providing practical guidance and inspiration to other organizations that are thinking about implementing data science in their organization. We thus leave out much to the technical details of the—sometimes quite advanced—solutions we have provided to our clients.