Sharing data with financial institutions
We share large amounts of data with financial institutions. Collaboration projects help us leverage data in our supervision more smartly. Prime examples are Dataloop, Know your Customer and Biodiversity projects.
Data Science Hub
Financial institutions share large amounts of data with us. In supervision, we can make smarter and better use of all data they submit to us. The Data Science Hub helps us do so by supporting data science projects and activities. Projects we support include Dataloop, Biodiversity and Know Your Customer. Find out more about the Data Science Hub.
We do not share any personal data
We exchange large amounts of data with financial institutions. But we do not exchange all data, because we always look at the big picture. In our reports and analyses we use data on a financial institution’s turnover, profit or loss, and cost items. Suppose a bank provides us with information on the payment behaviour of its customers, this will not enable us to identify individual bank customers. Personal data will always remain confidential.
We have developed the Dataloop application to improve data quality in supervisory reporting. Dataloop does this by centralising and visualising data from different sources. It also offers several feedback loops, for example between analysts and machine learning tools. In addition, such loops will soon be available between DNB and financial institutions and other supervisory authorities. Dataloop achieves 20% efficiency gains in compiling statistics. Also, machine learning allows supervisors to detect new patterns, which enables them to focus their efforts.
Making our economy more sustainable is high on our agenda, We recently investigated biodiversity loss and its impact on the financial sector. In our Indebted to Nature study, we show how different sectors depend on biodiversity, using innovative visualisations.
The know-your-customer principle
Financial institutions act as gatekeepers. They must prevent criminal money from going through the financial system. To do so, they must apply the know-your-customer principle. But how does a bank monitor millions of transactions a day, and how does DNB supervise those processes? This involves data science. Smart technologies allow us to spot potentially high-risk transactions in a single large database. An algorithm that makes outliers stand out from the rest helps supervisors focus their examinations.
The chart plots corporate bank customers, their wealth and the sum of incoming transactions. Each dot in the plot represents a customer. If the algorithm considers customers unusual it marks them red.
Using data in research and advice
We also aim to use data in a better and smarter way in our activities as a central bank. Higher-quality data analyses help us prepare better economic estimates, and they improve our understanding of the economy and inform our advice even better. Find out more about data science in our research.
Collaboration and contact
Our Data Science Hub is always on the lookout for collaborative opportunities, both with other supervisory authorities and with financial institutions. We are open to new ideas and suggestions from the financial sector. Please feel free to share your questions, comments and suggestions with us. Our email address is: email@example.com.