A group of 11 industry associations from around the world has published a letter supporting a set of principles developed by the International Swaps and Derivatives Association (ISDA), aimed at improving consistency in regulatory reporting standards for derivatives.
The 11 associations signing the letter include the Alternative Investment Management Association (AIMA), the British Bankers’ Association (BBA), the German Investment Funds Association (BVI) and the Australian Financial Market Association (AFMA).
The ISDA principles call for derivatives reporting requirements to be harmonised across borders and for the further development and adoption of global data standards. They were developed to address challenges that have emerged in the cross-boarder implementation of derivatives reporting rules.
Progress has been made in meeting the G-20 requirement for all derivatives to be reported to trade repositories to increase regulatory transparency, an objective the associations support.
However a lack of standisation and consistency in reporting requirements within and across jurisdictions has led to concerns about the quality of the data being reported – poor data quality could reduce the value of the data for regulators. Differences in reporting requirements could also increase the cost and complexity for firms that have reporting obligations in multiple jurisdictions.
Associations believe the ISDA data reporting principles will result in greater consistency in the content and format of the data being reported, which will improve regulatory transparency. They also believe similar principles will benefit global trade reporting requirements beyond derivatives, and that lessons learned from derivatives reporting should be applied more broadly.
Dev Bhudia, vice president of product management at enterprise data management firm GoldenSource, commented: “The call for regulators to look at data accuracy and integrity comes at a welcome time.”
He added: “But while looking for accurate data is all well and good, alone it isn’t sufficient. The quality of data must be actively managed. This requires efficient workflows for data errors, as well as a framework for continuous improvement to ensure that the quality of information remains at acceptable levels.”
