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Mukul Pareek, CISA, ACA, AICWA, PRM
In the world of market and credit risk, scenario analysis is used as a part of stress testing. Stress testing is mandated by national regulators and central banks, and takes the form of asking financial institutions to consider the effect of adverse scenarios on their capital and solvency. Scenarios include historical events, such as market crashes and debt defaults, and hypothetical scenarios, such as larger-than- expected moves in interest rates, housing prices or foreign exchange rates.
In the world of operational risk, scenario analysis is used in combination with the loss distribution approach to estimate operational risk capital under the Basel framework.1
For technology risk managers, scenario analysis can be a useful tool to identify, understand and articulate the technology risks faced by their organizations. Taken a step further, it can also be used as a tool to quantify and express a technology-value-at-risk number by expressing future losses in the form of a loss distribution.
In essence, scenario analysis consists of identifying future “what-can-go-wrong” situations that can cause a loss to an enterprise. This is something most technology risk managers already do as part of their daily task of explaining controls to business managers (e.g., when explaining risks or audit issues or when requesting new investments in security). What scenario analysis allows us to do is to consciously understand what adverse events can occur, explain how controls prevent (or, in some cases, are unlikely to prevent) unfavorable outcomes and explain how bad circumstances can get within a reasonable range of probability.
There are a number of reasons why technology risk managers and analysts need to consider scenario analysis as part of their risk management tool kit:
At its essence, scenario analysis is not all that different from risk analysis, with the notable difference being that multiple individual risks are required to combine together to create a comprehensive and plausible scenario. A scenario follows in the tradition of storytelling, whereas enumerating risk at a granular level is more of an exercise for the risk- and control-literate risk manager.
Broadly, there are two ways to identify scenarios: first, through an analysis of historical events, and second, through the construction of hypothetical yet plausible adverse events that may reasonably occur. Taken together, scenarios should be comprehensive and updated from time to time.
Scenarios based on historical events may include real events that happened to the organization or its peers (e.g., a large compromise of its billing systems revealing sensitive personal information). Generating hypothetical scenarios requires judgment, skill and a good understanding of the business. Hypothetical scenarios are important because they allow for the completion of the gaps left by historical events.
Scenarios based on historical experience need no explanation as to their plausibility. Hypothetical scenarios can be made plausible by seeking input from business managers who should play an active role in identifying them.
Expected losses are losses that are considered part of the cost of doing business, and arise year after year. They are characterized by a high frequency of occurrence and a low impact. An example would be average annual credit card fraud events experienced by a bank. Up to a point, these are just ordinary losses that are absorbed as part of the cost of doing business. The product is priced to include the occurrence of expected losses. These are governed by the law of large numbers. Unexpected losses include events such as a large-scale data breach.
Scenario analysis should not cover expected losses. Scenarios should be directed toward high-severity, exceptional and infrequent events. The nature of the technology risk universe means it rarely has to deal with expected losses; nonetheless, this is an important point to make so that technology risk managers do not become too engrossed with the details of ongoing transactional events.
At the very minimum, a scenario should include:
People have a generally optimistic bias toward their perception of their own competence and good fortune. This bias is likely to be reflected in any scenario-analysis session that a technology risk manager organizes—in the form of lower expected frequency of occurrence or severity.
One possible way to correct for this may be for the risk analyst moderating the scenario analysis not to focus on the enterprise, but to talk about similar organizations or competitors (i.e., how likely is such a scenario at the top four or five competitors, and if they were to suffer a loss, how much is it likely to be?). Any internal loss data or anecdotes of actual occurrences may help further align perceptions to reality.
Scenarios should cover the range of known technology risks that the business is likely to face. Documented controls should address one or more of these scenarios, and if the technology risk managers find controls that do not address a scenario, then either the universe of scenarios is incomplete or the control is redundant. Under each of the broad categories, such as process and workflow errors, information leakage events, business continuity events and external attacks (these may differ across organizations), there would be a number of scenarios.
Compiling a list of acceptable risk scenarios including all the attributes described previously is not a trivial task and requires sponsorship, cooperation from P&L managers and an understanding of the business by the technology risk manager. Scenario building may be carried out in a conference room setting with the technology risk manager or analyst leading the agenda.
In many cases, the scenario-analysis exercise is a valuable end itself. In some cases, the risk manager may choose to perform additional quantitative analysis by calculating a technology-value-at-risk number, as detailed in the next section.
Once scenarios have been identified, together with their expected frequency and severity, as explained in the previous section, these estimates can be converted to estimates of losses at different confidence intervals, similar to value at risk. We will call it the technology value at risk to distinguish it from the more common measure of financial risk.
The steps to determine a technology-value-at-risk number are:
These steps can be performed in Excel, or in a mathematical package such as R. While Excel is a great environment for prototyping and solving less-complex problems, R is more suitable to heavy-duty work. The decision of which to use would depend upon how widely and repeatedly the technology risk manager needs to use the risk model, and available skill sets.
To summarize, the technology-value-at-risk calculation includes the following steps, as visualized in figure 3:
Scenario analysis, even if carried out without any additional quantification, can be a useful exercise to bring together technology risk practitioners and the business that they serve. It can generate the right conversations and engagement and focus management on issues that truly matter to the organization. It can also help evaluate controls in the context of real business situations, and help identify controls that can be safely dropped without an inordinate increase in the risk. If scenarios are converted to a technology-value-at-risk number, the enterprise gets the additional benefits of being able to evaluate the monetary impact of adding or removing controls.
Yet the approach is not without limitations. Real life is complex, and adverse outcomes inevitably compound. Additionally, the impact from scenarios often extends beyond technology. It is difficult to successfully model strategic, legal and reputational risk areas that often accompany technology risk events. A modeler would need to bear these limitations in mind as part of any scenario analysis.
1 Basel Committee on Banking Supervision, Basel II: International Convergence of Capital Measurement and Capital Standards: A Revised Framework—Comprehensive Version, www.bcbs.org2 Solver is a native Microsoft Excel add-in that allows complex problems to be solved using optimization routines. It may be enabled under the Add-Ins menu in Excel.3 R is a popular open-source software used for mathematical and statistical analysis. It can be downloaded from cran.r-project.org.4 Monte Carlo simulations are a statistical method where data points are obtained by repeated random sampling. This allows for simulating complex systems and interactions that may be difficult to express analytically (e.g., as a clean formula).
Mukul Pareek, CISA, ACA, AICWA, PRM, is a risk professional based in New York, USA. He has more than 20 years of audit and risk experience in industry and financial services. He is copublisher of the Index of Cyber Security, www.CyberSecurityIndex.org. He can be reached at firstname.lastname@example.org.
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