MiFID II, UCITS IV, Dodd Frank and Basel III Driving a Rethink of Massive Data Management

As Basel III, MiFID II, UCITS IV and the Dodd Frank Act are finalised and/or come into force, ever higher data requirements have been thrust on financial institutions that require them to meticulously track the origin of data, its transformation over time and the persons or processes responsible for changing it. Some reports have estimated that at least 70 new regulations governing the capital markets will come into force in Europe between 2012 and 2013 with more than 300 in the US over the same period.

The power to retrieve and consolidate data from several sources in real time so as to feed data warehouses, risk engines and therefore compute current risk exposure is thus crucial now more than ever. This is especially so when one considers the current drive toward creating a central OTC market so as to prevent another cataclysmic financial markets crisis as was witnessed in 2007-2009. New supervisory rules are compelling firms to extract and report on enormous quantities of trading data without compromising the quality of such data.

The enterprise data is fed into pricing and risk analysis data warehouse models. The sheer volume of OTC transactions is just one of the many forms of massive data that characterize the capital and financial markets. A recent poll by MoneyMate of buy-side market participants showed that 80 per cent of respondents were not prepared for the imminent regulatory changes. 75 per cent of polled firms considered the Dodd Frank Act a serious cause for concern.

To be fair to financial services organisations that are still unprepared for impending regulatory changes, new rules such as Basel III and MiFID II go through several revisions and amendments before the final framework becomes clear to all stakeholders. But even after the new rules become clear, disparate systems between customer facing, middle office and back office functions make it difficult for financial institutions to accurately compute risk exposure, automate collateral assignment and put in place the systems necessary to achieve real time position valuation.

Indeed, for global financial market players, one of the biggest challenges facing their risk management and compliance teams is evaluating exposure across the bank’s entire business. A recent poll by Simcorp showed that 30 percent of buy side market players admitted that they would need days or even weeks to compute their entire organization’s risk exposure.

To put such drawn out calculation in context, this would mean that in situations such as the implosion of Lehman Brothers and Bear Stea
s, 30 percent of buy side players would be slow to react because of a lack of timely risk information.

Following the control gaps that were so dramatically laid bare by the 2007-2009 financial crisis, supervisors have directly or inadvertently drawn greater attention to market data as they aim to change the current OTC derivatives market into an exchanges-traded model. One way that this is happening is the drive to have a system of LEIs (Legal Entity Identifiers) that will be used to tag transactions to respective counterparties.
Regulators must not be left behind in adopting massive data management technology

Interesting though is that whereas new rules have continued to drive innovation in the capture and management of enormous amounts of data, financial industry supervisors themselves are often slow in implementing such techniques. Yet, the more efficient regulators can capture and analyze enormous data, the faster they will be able to identify and defuse systemic risk.

In fact, some analysts of the 2007-2009 financial crisis have laid the blame not on weak laws but on weak supervision. Such analysts have argued that all the data that was necessary for supervisors to nip in the bud the ballooning risks from derivatives and subprime mortgages was available but was never acted on. While not everybody will necessarily agree with this line of thinking, the controversy and eventual dissolution of the Office of Thrift Supervision in the US does lend some credence to this proposition.

Still, some financial market supervisors are taking steps to efficiently capture market data. The SEC (Securities Exchange Commission) in the US for instance, has floated the idea of a Consolidated Audit Trail. The CAT would be based on collating information from FINRA and every exchange into a central data repository. The information would be on every order, every quote and every reportable event affecting each order and quote. In the event of a sudden crash, the SEC would have the real time data necessary to quickly see what happened as opposed to waiting several weeks to decipher what exactly happened.
The way forward

The new regulations call on both supervisors and banks to adopt a sophisticated approach toward the capture and aggregation of data from multiple sources, report it and maintain the data’s history to allow for future audit. To do that, institutions must take an enterprise-wide inventory of data, identify the attributes of such data and isolate the fields that will be relevant for regulatory reporting.

One of the key challenges will be the need to harmonise time stamps especially when the data is originating from different systems. Risk managers must work with technology staff to ensure all data that makes its way into the risk data warehouse is time-consistent. Remember that the best case scenario is for risk data capture and position appraisal to take place in near real time.

Ensuring time consistency can be difficult when one factors the possibility of data queuing in different systems which may ultimately affect how timely risk managers can generate a position statement. Still, a sophisticated system would take these dynamics into consideration so as to guarantee that the final risk reports are an accurate representation of current data.