In the rush to achieve regulatory compliance and straight-through processing (STP), have firms failed to pay sufficient attention to the risks associated with poor data quality?

That would certainly seem to be the case. Despite the milions invested by financial services companies in middleware to integrate key trading systems, one in ten trades stil fail on first settlement attempt, according to 2008 research from market analyst company TowerGroup. And when that occurs, the chief culprits are inaccurate or inconsistent reference data and poor data management processes. These problems, says TowerGroup analysts, account for some 60% of failed trades.

While market data provides real-time pricing information that is constantly changing,reference data is the static information used in the administration of a securities transaction. While it may be static, it is also very broad, encompassing historical pricing information (such as an end-of-day price on a given date), security identifier codes, the exchange a security trades on, names and addresses of counterparties, and details of corporate actions, such as stock splits or proxy votes.

To add to the complexity, reference data is typical y sourced from a range of internal and external providers, and fed into data siloes scattered around the organisation, for consumption by employees in dif erent departments. In other words, there’s no single
version of the truth from which everyone works.

And when inconsistencies or inaccuracies in reference data arise, exceptions in the trade lifecycle occur, leading to increased operational risk, lost revenue opportunities, financial liability and the need for expensive and time-consuming manual trade duplication and reconciliation processes.

Given its vital role in processes such as portfolio valuation and NAV [net asset value] calculations, it’s clear that, when sourced and managed in this way, reference data is a key contributor to operational risk.

That’s even more true in a period of market volatility. Take, for example, the experience of one sel -side bank that was recently forced to close out a hedge fund. While looking to sel the col ateral it had taken out against positions held in that fund, it discovered that, while front-of ice traders were working from one set of pricing data, its risk management staf had a completely dif erent version, leading to huge, unnecessary cost.