Learning is a benefit that is important enough to describe separately!
Like much financial and credit model development, scenario analysis is research as much as development or construction. Scenario analysis leads to a better understanding of the relationships between the firm and its environment and risk factors. Note that these relationships change through time as the firm, industry, social and political conditions, technology, and other factors change.
Learning involves:
1. Identifying hidden threats
The actual or precise benefits of learning aren’t usually known prior to the exercise; however, experienced practitioners know that they should be anticipated. The identification of otherwise hidden threats includes the opposite of robust mitigants: precautions, insurance or contingencies that are beneficial or are intended to be beneficial in one scenario, but harmful in another.
2. Identifying hidden opportunities.
The movie, “A Christmas Story,” provides an excellent example: the protagonist’s younger brother’s (Randy’s) snowsuit. It was bulky and thick enough to protect against the snow and the cold temperatures of a Fort Wayne winter, but it wasn’t very robust as he could barely move in it. When he fell, indeed, he could not get up on his own.
Consider the extremely tragic case that did not occur in the movie; Randy could have frozen to death because his snowsuit was designed to protect him from the cold at the expense of maneuverability. Had he fallen in a remote location, by himself, he could have died from hypothermia. In banking, examples include hedges that are designed to work on average but fail miserably in certain, low-probability scenarios creating losses on both legs.
3. Identifying or seeing linkages, e.g., positive and negative correlations that amplify or mitigate risks, respectively.
Counter-party credit is an example of the third type of learning: realizing that the insurer may be insolvent when insurance or hedging is most needed and would otherwise be valuable.
4. Identifying enterprise weaknesses to determine and assess harms–and, therefore, act across different environments.
Identified enterprise weaknesses frequently involve knowledge and evidence, especially data that would be useful for decision-making, including determining appropriate precautions and contingencies. Most organizations keep the correct data to prepare customer statements and financial reports. Unfortunately, during scenario analyses, firms frequently discover that they haven’t kept the data fields needed to: (1) estimate losses or (2) identify contingencies. For example, credit and demographic characteristics are frequently missing, which makes (1) loss estimation and (2) the determination of appropriate contingent actions, like line management, either difficult or impossible.