The UK’s markets regulator,the Financial nduct Authority (FCA)has been examining a regulatory reporting problem that everyone is familiar with.Regulators define requirements.mpanies interpret them and then identify how they can source that information,then developing systems and processes that (hopefully)allow reliable and automated ways to routinely submit the required data to the regulator.Over the last decade the amount of information that regulated panies provide to regulators has grown enormously.

From the perspective of regulated panies this all involves very nsiderable expense and very nsiderable human effort to deal with new or changing requirements.From the perspective of the regulator it almost guarantees that the data that is being acquired involves different interpretations made by different experts within different panies.It also means that introducing change to regulatory reporting requirements always takes significant amounts of time.

What if there was a way to have pany systems read new reporting requirements,interpret them aurately and automatically llate the information required?uld the ing generation of AI and linguistic analytics systems drastically rce the st of pliance?

In November last year,the FCA held a series of ‘TechSprint’workshops to examine how technology can make the current system of regulatory reporting more aurate,efficient and nsistent.The result was a very simple proof of ncept for a process that would allow firms’internal systems to interpret machine readable data requirements published by a regulator’s systems.The firms’systems would then map those reporting requirements directly to the data that they hold,creating the potential for automated,straight-through processing of regulatory returns.

A new call for input provides information on how this proof of ncept was developed and further asks for views on how the FCA can improve the process.The paper also seeks feedback on some of the broader issues surrounding the role that technology can play in regulatory reporting.The ment period will remain open until June,with a summary and proposed next steps published this summer.

Machine readable semantics lies at the heart of XBRL taxonomies,but the process of creating them tends to be driven through much more traditional processes than those that the highly disciplined designs the FCA has been experimenting with.This might be a very interesting new approach to data llection…it’s certainly something that XBRL International will be ntributing to,and may well be of interest to many others.

The FCA’s press release is here and you will can find the call for feedback here.

中文新闻：英国FCA寻找降低合规成本之法