A Calculated RiskA Calculated Risk

The Measurement Matrix

The Advance Measurement Approach (AMA) introduced by the Basel Committee for Banking Supervision to measure OR consists of three subcategories: the Internal Measurement Approach (IMA), the Loss Distribution Approach (LDA), and the Scorecard Approach (SCA).

The IMA is a more advanced approach because it lets banks use external and internal loss data as well as internal expertise. The LDA and SCA are very similar as both approaches are based on a statistical value-at-risk (VaR) model. However the difference is that, with LDA, only internal or external historical loss data can be used for estimating the distribution functions. With SCA, banks are also allowed to apply expert knowledge to estimate the distribution functions.

Using the statistical approach, FSIs can capture information about lost checks or errors in transferring funds (such as their frequency and severity) to generate loss distribution using Monte Carlo simulation. The mean and a certain percentile point are calculated in order to estimate expected and unexpected losses. This measurement of VaR is used to allocate economic capital to OR.

Direct losses related to OR events can be measured with the statistical approach, while scenario analysis helps calculate indirect or potential losses. Operational VaR methods can be validated in two ways: back testing or statistical tests. It may be difficult to conduct back testing due to data availability compared with market risk, but it's possible to use statistical testing to ensure the robustness of OR measurements.

Although the statistical model lets FSIs measure risk, it doesn't provide any tools or clues to reduce risk. In addition, a statistical model is beyond the grasp of many bank staff and may act as a barrier to the desired level of understanding of OR. Moreover, due to the relative scarcity of internal loss data, the statistical model must rely more on external data.

With the VaR method, resource allocation becomes more effective: Its priority is on each loss type in each business line for enhancing daily OR management and conducting internal audits in a more risk-focused manner. With scenario analysis, potential losses can be measured so that contingency plans can be created in order to minimize potential damage.

An increasing number of banks are in the process of enhancing loss data collection for not only measurement of VaR but also for robust risk management in order to upgrade OR management both quantitatively and qualitatively.

One of the challenges in today's business climate is using external loss data to supplement internal loss data. Internationally, some loss data consortia have started to reveal FSI loss data so member banks can use external loss data.

The SCA avoids many of the problems inherent in analysis of historical data by capturing the knowledge and experience of the experts who design the scorecards. Data collection problems, however, are transferred to the collection of risk indicators, which can also suffer from quality issues. And the reliability of the output becomes dependent on the experts employed to design the metrics and weightings within the scorecard.

A badly designed scorecard could produce results that are completely inconsistent with reality, intuition, and the actual history of losses. Relying completely on scorecards could be compared to driving without keeping an eye on the rear-view mirror. An intelligent bank will always use a combination of methods, one supplementing or testing the other.

Toward a Cleaner Slate

The high-end priority for all financial institutions today is to maintain clean, quality data by investing in appropriate data collection systems. An organization's capacity to limit and measure risk will be much greater once these enhanced data collecting and intelligence tools are applied to better understand their customers' positions. Proper customer intelligence will help define and recover bad loans, which will in turn lead to effective debt management strategies that reduce the money required for capital allocation, as prescribed by Basel II for OR management.

Sabyasachi Bardoloi is the manager of Pinnacle Research Group at Pinnacle Systems, a technology consulting and solutions provider to Capital Markets firms.