IBM Data Governance Council Leads XBRL Initiative

In a move to provide businesses worldwide with consistent tools for measuring aggregate risk in the financial world and provide a real-time view of market exposure, the IBM Data Governance Council is seeking input from banks and financial institutions, corporations, vendors and regulators to create a standards-based approach to risk reporting.

Organizations have inconsistent methods and vague language for disclosing operational, market, and credit risk. These inconsistencies make regulatory oversight both extremely difficult and complex. The first step to enabling new transparency of risk and exposures in the financial services industry is semantic clarity, a precise method for consistently describing and reporting risk across all organizations. Such transparency could provide a new macro-economic tool and greater fiscal accountability for regulators, investors and Central Banks worldwide, making it easier to identify toxic assets on the books, mitigate fraud, help prevent wide scale fiscal crisis and rebuild confidence in financial systems.

The IBM Data Governance Council is exploring the use of Extensible Business Reporting Language (XBRL), a software language for describing business terms in financial reports, to risk reporting. XBRL could be used to provide a non-proprietary way of reporting risk that could potentially be applied worldwide. It is already widely used for financial reporting throughout Europe, Australia and Japan. The widespread use of this standard ensures adequate skills and understanding among firms and regulators.

"Creating a risk taxonomy using XBRL will provide a vocabulary and a common language allowing everyone to understand what risk means, and that's the first step in making it easier to calculate and report," said Steve Adler, chairman of the IBM Data Governance Council, in a press release. "When we have semantic clarity around the way organizations describe risk, incidents, events, losses, claims, exposures, forecasts and reserves, it gets easier to aggregate loss information, analyze it with standard actuarial methods, compare past exposures to present conditions and opportunities, and forecast potential outcomes," he added.