The US corporate actions market has long been characterised as paper-based and manually intensive, but it seems that much progress is being made of late to tackle the lack of automation due to the introduction of four little letters: XBRL.

Only a few years ago – but crucially, prior to the financial crisis – surveys which reviewed progress towards corporate actions automation highlighted the key drivers for automation as reducing costs and inefficiencies. Whilst important, these were essentially technical objectives and correspondingly the debate, and its participants, inclined heavily towards the operational parts of the business. There are important signs that this is changing.
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Increasing volumes and the complexity of reference data in the post-crisis environment have left the middle office struggling to meet the requirements of the current market order. Middle office functions must therefore be robust enough to be able to deal with the spectre of globalisation, an increase in the use of esoteric security types and complex investment strategies, as well as rising transaction volumes. This has led many from the buy side and the sell side to consider outsourcing strategies for some of these functions, offshoring, in-house development or the acquisition of new vendor technology.
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The global regulatory community has become increasingly aware of the data management challenge within financial institutions, as it struggles with its own challenge of better tracking systemic risk across financial markets. The US regulator in particular is seemingly keen to kick off a standardisation process and also wants the regulatory community to begin collecting additional data in order to better supervise systemically important financial institutions.
So, data is definitely on the regulatory radar and the Fed itself has been investing in its data infrastructure, including adding ex-Citi chief data officer (CDO) John Bottega to its ranks. If data collection and aggregation is required for regulatory reporting purposes then surely enterprise data management (EDM) is an obvious part of that endeavour?
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A new industry briefing and survey report from A-Team Group and GoldenSource
A-Team Group, a publishing and research company specialising in financial information technology, was commissioned by enterprise data management specialist GoldenSource to conduct research into the challenges of managing pricing and valuations data.
Throughout the course of October 2009, A-Team Group researchers interviewed senior-level specialists closely aligned to market data or valuations. Several spanned multiple responsibilities including oversight of client data, product information, and trading risk.
The interview sample was spread across asset managers (52%), Tier-1 and Tier-2 banks (32%), broker/dealers (11%) and custodians (5%).
Geographically, participants were dispersed across the United Kingdom (47%), Europe (21%), and the United States (32%). Over half of the respondents had global responsibility within their organizations.
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This year has truly been a year of change for the data management community. Regulators and industry participants alike have been keenly focused on the importance of data with regards to compliance and risk management considerations. The UK Financial Services Authority’s fining of Barclays for transaction reporting failures as a result of inconsistent underlying reference data is a case in point. Firms are now more aware than ever before of the dangers that are posed by failing to adequately manage this data: reputational and operational risk is at stake.
Against this background of intense scrutiny, financial institutions are beginning to crack open the door again on delayed projects to centralise and harmonise their internal reference data sets. These may be largely tactical at the moment but the increased interest in enterprise-wide risk management may prove to be a catalyst for enterprise data management (EDM) projects in the near future. There is also an increased level of awareness at the senior management level about the cost saving benefits of rationalising data feeds and systems. The driving force is to do more with smaller headcounts and tighter budgets.















