Central banks worldwide have been working to enhance the ways they collect and analyse data for supervisory purposes. Increasingly, this is pushing central banks to work more closely with industry to improve data tools.

“I think one of the big changes we have seen is this collaborative approach,” says Joanne Horgan, suptech product director with Regnology.

In a Central Banking podcast, Horgan cites the example of the UK, where the Bank of England and Financial Conduct Authority recently launched a project to overhaul data collection. One of the key elements of the project is for the regulators to work more closely with firms, including on the question of how to reduce the burden of reporting. That is “quite new”, says Horgan.

Supervisory technology is becoming a core part of how central banks conduct supervision, given the growing importance of data in the financial sector. But many central banks still collect data for particular queries at a high level of aggregation. That means for most new queries, they will need to send a new data request to firms.

The hope is that central banks can move towards more granular data collection, and greater levels of standardisation. Longer-term, Horgan notes, supervisors could achieve “real-time” collection as new data becomes available.

“Technology to help with regulatory compliance has been around for a number of years, but I think what we’re seeing is Covid-19 has accelerated the rate of digitalisation for central banks and regulators,” says Horgan.

Alongside growing digitisation of data collection, there has also been more digitalisation of communications. Covid-19 forced supervisors to find ways of carrying out more of their work off site. Digital communication tools can also bring supervisors and industry players closer together when collaborating on improved data tools.

Horgan envisages an “ideal state” where firms provide very granular data that goes into a centralised, standard data model. The central bank can then query the data without firms having to do anything.

“The technology I don’t think is necessarily the challenge … once you have agreed a data model at a granular level,” says Horgan. “More of a challenge is to agree that with the industry, and that’s where the collaboration is really important.”

Index

1:00 Setting the scene

4:10 Benefits of greater adoption of technology

6:30 Different routes to better data

11:30 Importance of suptech/regtech alignment

14:50 The ideal end state