By Mark Basch,
Senior Business Analyst,
Reference Data Product Development,
The turbulence in the global financial markets over the last 18 months has heightened awareness about the risk management practices of financial institutions around the world. Firms are focusing on many types of risk, including liquidity, market and operational risk, in addition to traditional portfolio metrics. But of the myriad risks faced by firms, one of the most basic has proved to be one of the most challenging â identifying exposure to a given entity.
On the surface, identifying risks related to your exposure to a given entity would seem to be a fairly straightforward process. But this risk goes far beyond direct investments in the securities of a company - it can include credit default swaps on the entity, trades where the entity is a counterparty, or potential lost revenue if the company was a client and it were to go out of business. In addition, you need to account for dealings with affiliates of the entity.
The significant impact of failing to understand a firmâs exposure to an entity received added attention during the credit crisis of late 2008 when firms struggled to identify their exposure to Bear Stearns and Lehman Brothers. Through discussions with different financial institutions, we became aware that it took some firms more than six weeks to identify all their exposure to Bear Stearns and/or Lehman, which made it challenging to make effective business decisions without knowing just how much of the firmâs capital was at risk.
Unfortunately, knowing that you have a problem understanding your exposure does not immediately enable you to solve it. And with a substantial amount of entity information being proprietary to a specific firm, how can a risk management professional implement an automated, repeatable process that cuts back on manual efforts and errors?
To get started, a risk management professional can identify those components of entity exposure that can be subject to an automated process. The security exposure component has one key characteristic that makes it an excellent target for automation.
This characteristic is the use of standard identifiers, such as CUSIPÂ® and SEDOLÂ®, for the vast majority of equity and debt securities. These standard identifiers allow for the creation of a pre-defined map that links the security to its issuer.
Imagine you are asked to identify your exposure to Bank of America with respect to a portfolio of financial securities that looks like Fig1 (see above).
A quick look at these names reveals some that are immediately recognizable as Bank of America obligations. You also may recognize entities that were recently purchased by Bank of America, such as Countrywide Home Loans and Merrill Lynch. However, due to the myriad of mergers, acquisitions and name changes that have occurred over the last decade or longer, many of the original issuer names may no longer bear any resemblance to the current obligor.
Without business entity data, you would be forced to research each one of these names on a case-by-case basis to find out what happened to the issuing company. This is a manual and time-consuming process that needs to be done every time you add a new security to your portfolio. With a solution such as Interactive Dataâs Business Entity Service, this process can be automated. In order to effectively identify your exposure, you need to understand the current ultimate parent of each issuer. Using business entity data, you can access the D-U-N-SÂ® identifier and name of the ultimate parent.
Business entity data can help you classify the Bank of America obligations so exposure to the securities in the portfolio can be easily identified and calculated.
This example helps demonstrate the value of Interactive Dataâs Business Entity Service. By identifying the ultimate parent we were able to identify and calculate the exposure to a given entity for a sample portfolio. While we used a small portfolio in this example, the same process would apply no matter the size. A service such as this allows for the automation of a manual process, greatly reducing the time involved. Given the increasing complexity of the financial markets, the demands on risk managersâ time will only grow. With tools like the Business Entity Service, more resources can be dedicated to other aspects of risk analysis that are less suited for automation, such as analyzing counterparty or client exposure.