The purpose of a scenario program is to prepare the firm to manage specific risks in detail and to assist in calculating capital requirements by generating frequency and severity data points
The document does not specify an exact number of scenarios a firm should use. The ideal number would depend on the firm’s specific risk profile, size, and complexity, as well as the requirement to cover a wide range of risks adequately
Firms undertake scenario analysis to understand specific risks in detail, to ensure preparedness for potential events, and to generate necessary data for operational risk capital calculation
When determining a scenario programme, a firm should consider its objectives, the range of risks to be covered, the resources available for conducting scenario analysis, and how the outcomes will be used in risk management and capital planning
Approaches for scenario analysis may include workshops, interviews, questionnaires, the Delphi method, research and validation processes, external data analysis, and simulation games
Difficulties include ensuring participant engagement, managing biases, achieving a common understanding of scenarios, transforming qualitative assessments into quantitative measures, and obtaining regulatory acceptance
For a new scenario, a firm should collect data on potential frequency, likelihood, impact, and severity of the event, as well as any relevant internal and external loss data
Types of bias may include anchoring bias, availability bias, confirmation bias, optimism or pessimism bias, and groupthink, among others
Validation can be done by comparing scenario outcomes with historical data, expert opinion, and other operational risk tools, as well as through peer reviews and regulatory feedback
RCSAs focus on identifying high-level risks and controls within specific units, while scenario analysis dives deeper into understanding specific risks and preparing management for potential events. Scenario analysis also requires exploring additional data points for capital calculation
Loss data, including internal and external losses, is used in scenario analysis to illustrate how scenarios may manifest and to populate the loss distribution ‘tail’ with frequency and severity data points