Viewpoint | Data management | Stephen Engdahl
Data management to improve risk management
Stephen Engdahl, Senior Vice President, Product Strategy, GoldenSource.
Risk analysis is not new. Our industry is built around techniques to achieve maximum return with minimal (or optimal) risk. And the risk discipline requires copious amounts of timely, high quality, relevant data. Institutions now recognise the importance
of good data management practices in order to improve their risk practices, and more than ever before they are prepared to do something about it. Risk analysis is a common driver of many active GoldenSource client projects focusing on data management, data quality, and data governance. But if risk analysis is such an established practice, then why are so many institutions just now focusing on data as it relates to risk? What changed?
Navigating an uncertain world The markets have taught us many lessons over the last five years, including the importance of expecting the unexpected. Market and economic events relevant to risk management, i.e. counterparty defaults, market volatility, mortgage crises, and sovereign debt restructurings, stressed the data management practices of most organisations. Consider even the relatively simple task of measuring exposure to various dimensions – what was your exposure to a failed counterparty, including all its subsidiaries and related entities? To securities linked to subprime mortgages? To Greece? Now, imagine answering these questions in an environment where positions are recorded in multiple systems, counterparty hierarchy is not well defined, and critical reference data attributes
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exist in spreadsheets or separate incompatible security masters, not linked to counterparty and position data. What should be a simple task becomes a significant problem unless the right data infrastructure is in place. Easy access to accurate risk data cannot be
ignored, because we live in a volatile world. “100-year floods” are supposed to happen no more often than once every 100 years, but now they’re occurring with alarming frequency. In the financial markets, the same goes for formerly “unthinkable” events such as major institution and sovereign crises. This situation is particularly stressful without
a solid data foundation which aggregates and standardises internal position and transaction data, and links it with clean counterparty, hierarchy, instrument and underlying data so that measurement and analysis can proceed. After facing a series of external events and realising that answering critical questions took far too long, institutions are recognising that the time to aggregate data, standardise it, and monitor it for quality is before the next question arises, not when it arises.
More regulation, please? Multiple jurisdictions worldwide are asking for more data to support the decisions made and positions taken by financial institutions. Centrally clearing derivatives changes the risk profile of these instruments. Certain trade reporting must be enriched with Legal Entity Identifiers (LEI’s) and Unique Product Identifiers (UPI’s). Solvency
Best Execution | Summer 2013
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