The financial industry as been shaken by the recent credit and mortgage fallout and has spotlighted the critical role of risk information management within a financial organization. Risk information management is a key component of a financial firm's risk infrastructure. However, the recent crisis in the financial system was in many respects a crisis of information. It was the lack of quality information, the ability to model this information, and finally, the ability to provide complete transparency to this information, which was at the heart of unmanaged systemic, liquidity and counterparty risk. Financial firms who seek to overhaul their risk management methodologies and governance must address these issues.
It begins with quality information. Risk measurement requires high-quality historical information for modeling. Many structured products and other derivatives have special features including intricate priorities of payment, multiple hedges, complex definitions and multiple cash flow triggers that impact risk. Unfortunately, this data is often locked up in PDF’s, and is usually incomplete.
Most firms today seek to address this issue by sourcing this manual process through a third party. There are processes that can be automated, and processes that are still manual. This means that someone is gathering this information and entering it in by hand. To put this in context, in 2006 there was $520 Billion worth of CDO’s issued. Who was reviewing the details within each contract to verify the accuracy of the underlying information, or checking the actual terms in the credit default swap linked to a particular tranche?
In addition to ensuring the quality of data, a firm must be able to accurately model this information. Again, this becomes tricky in dealing with structured products such as CDO’s that themselves may contain other pooled instruments within them.
Secondly, the necessary information to understand risk exposure often must be assembled from multiple sources. Therefore, the underlying data model must be able to aggregate information from the firm level with look-through analysis down to its lowest elements.
Nor is this information viewed in isolation. Instead, risk managers need to aggregate information across their organizations in order to understand the relationship between systemic, liquidity and counterparty risk. This information is then viewed with performance attribution to determine how well their managers adjusted to market changes.
Therefore, the challenge is that the data model used for this aggregation must be able to incorporate all relevant enterprise-wide information. It is critical for a firm to make certain that the design their risk data model understands how enterprise-wide information relates to one another; especially if they wish to leverage sophisticated business intelligence technology for analysis and reporting.
A chief risk officer must have a solution that analyzes information across the firm all the way down to the underlying elements of a strategy or position. This capability must be flexible to allow the user to follow a chain of thought in their analysis. And, it must allow analysis across multiple dimensions such as time, risk, asset allocation, and performance attribution. You see this today, as financial firms are required to conduct more thorough stress testing and scenarios analysis. What happens if interest rates go up x% or if half the instruments in a portfolio terminate their contracts in the following year?
In addition to their own use, many firms are being asked to provide this data for reporting to regulatory bodies for macro-prudential risk analysis, or to their clients who are demanding greater transparency to their underlying funds. We see these trends significantly gaining momentum. Therefore, a firm’s enterprise-wide data management solution must also incorporate the ability to extract the required data and produce the appropriate reports to meet these growing requirements.
Disclosure: No positions