Data integrity and post-crisis regulatory reform

Mark Brennan is head of business development Americas at ITRS

The integrity of data in capital markets – be it, for example, price data, trade facts, collateral balances, or other key business information – has long been a fundamental concern of technologists and business stakeholders. But how do we define integrity? asks Mark Brennan

As in the universe of human ethical behavior, the precise definition can be hard to nail down – though, as with human conduct, we practitioners feel we recognize it when we see it.   What does it mean for data to be whole, sound, or perfect?  Clearly, accuracy is paramount.  As long as there have been information technology systems serving financial services, data integrity has obtained as a core principle:  each business fact must be mapped to understandable datum; system functionality must produce, or derive, correct data.  But an adjacent principle is transparency:  unless behavior – or information – is clearly delineated or articulated, there can be no integrity.

Thus the global regulatory drive for transparency in financial markets has in turn given way to an urgent need for data integrity.  Going back to the G20 summit in Pittsburgh in September 2009, following the great recession, the world’s leading countries agreed that there needed to be more transparency and risk mitigation for the global derivatives market, in the form of organized clearing, reporting and trading.  These principles were later enshrined in Dodd-Frank’s Title VII, and are at varying rates of progress in the EU in the form of the EMIR and MiFID II regulatory initiatives.

The market drivers putting pressure on data integrity

The foundation of each of the three prongs of derivatives reform (clearing, reporting and trading) depends critically on data integrity.  Although technology systems dedicated to specific functional areas should in theory produce and process correct data, the onslaught of new regulation compels market participants to have a stronger grasp on the flow of data through their systems; the data needs to be collected and understood – correctness can’t be assumed.

Consider the following:

Clearing new derivatives products is no trivial task, and involves the integration of new flows of data, including connectivity to the clearing house for trade acceptance, reconciliation, valuation, and movement of margin.

Trade reporting, which in the EU is a big focus of ESMA now, involves connectivity to one or more trade repositories; but more importantly, both buy-side and sell-side trading counterparties must capture all the correct details of their trading data for timely transmission, and the trade repositories must be able to reconcile both sides of each trade.  Arguably the industry needs better standardized data taxonomies for such data, but that doesn’t change the fundamental need to capture correct and accurate data.

Finally, SEF trading in the US, or the forthcoming MTF/OTF trade mandate under MiFID II, means market participants must connect to new venues, and be able to see and action potentially disparate sources of pricing data.

The challenges of getting it right

Each of these initiatives demands that market participants be able to capture and understand various streams of data.  The CFTC has made it clear that it expects banks involved in the trading and clearing of swaps to have straight through processing in place (under Regulations 1.73 and 1.74) and be able to process swaps within prescribed timeframes.  This implies that in addition to having the pipes in place for the flows, clearing brokers must have visibility (in the form of events and metrics) of the data.  So too for trade reporting:  market participants must be able to know which trades need reporting, and what data to capture.  Finally, trade execution involves significantly more than simple SEF connectivity, as market participants will need to watch disparate price feeds and make intelligent trading choices.

The ramifications if it goes wrong

The need for data integrity across each area (clearing, reporting and trading) has concrete regulatory compliance ramifications.  Each mandate relies implicitly on participants using correct data, and bad data potentially opens participants up to unwanted regulatory scrutiny, if not worse.  But in some ways the financial risk, especially for clearing and trading, is even greater.  As an example, a trader who does not understand he has latency on a specific SEF price feed may be pricing his own swaps incorrectly, resulting in material losses – data streams in, but its staleness corrupts the trader’s view.  Clearing flows incur risk for FCMs if they fail to monitor specific exposures and margin obligations.

And, most importantly, what can be done?

All of the regulation-driven market changes require technology solutions.  No doubt, there is tremendous complexity involved in mapping out the functionality involved in complying with the clearing, reporting and trading mandates, and implementing technology that meets the requirements.  As the industry rushes to comply, however, it’s clear that many firms simply lack tools that can holistically collect, analyze and action data across their set of workflows.  Thus, unfortunately, the integrity of the data across these streams is often dubious – a point highlighted by both the CFTC and ESMA regarding trade reporting.  Tools that can manage large sets of data – in all of its so-called volume, variety and velocity – are essential; but equally important is the additional dimension of veracity.  As the landscape changes, market participants need advanced technology to cope adequately with all four dimensions of data.