For the financial services industry, risk and compliance always have been an inherent part of doing business. But in recent years, scandals and market uncertainty have put risk and, by association, the consequences of poor data management, in the spotlight. The problem is fundamentally inconsistent information on securities, customers and counterparties identification held in an array of systems both within and among firms.
Any inconsistency at any point in a transaction results in a high number of transaction breaks that need manual intervention to fix, settle and, ultimately, reduce a firm's exposure and risk. The cost of such inefficiency in bottom-line results and lost opportunity is self-evident.
Today's competitive pressures and the stringent regulatory climate demand that financial services firms can identify and manage every conceivable credit exposure that they have with their customers and counterparties. The need to understand at the deepest level with whom they are doing business, what the business linkages are and what the profit potential is across the enterprise is compelling. In fact, notorious corporate scandals of recent years have highlighted the risks of not being able to unravel the sometimes-complex thread of customer linkages in time to make a difference.
Obtaining a single customer view, or indeed counterparty view, so that the complete credit exposure per ultimate customer or counterparty is known across the enterprise must be today's ultimate goal. This requires a complete legal hierarchy to clarify who the ultimate party is behind a company. If there is a guarantor behind the counterparty, the creditworthiness of the guarantor also must be examined.
Yet, in spite of increased focus and investment on data management by financial institutions, data quality still is a fundamental issue that needs to be resolved. Unless data is clean, consistent, complete and, most of all, trustworthy, the most sophisticated analytic tools and methodologies in the world will not be able to overcome the consequences of poor data governance. Lack of confidence in data has a number of important implications for exposure management, risk management, compliance, customer service and, ultimately, business credibility.