The use of models invariably presents model risk, which is the potential for adverse
consequences from decisions based on incorrect or misused model outputs and reports.
Model risk can lead to financial loss, poor business and strategic decision making, or
damage to a
bank's reputation. Model risk occurs primarily for two reasons:
- The model may have fundamental errors and may produce inaccurate outputs when viewed against the design objective and intended business uses. The mathematical calculation and quantification exercise underlying any model generally involves application of theory, choice of sample design and numerical routines, selection of inputs and estimation, and implementation in information systems. Errors can occur at any point from design through implementation. In addition, shortcuts, simplifications, or approximations used to manage complicated problems could compromise the integrity and reliability of outputs from those calculations. Finally, the quality of model outputs depends on the quality of input data and assumptions, and errors in inputs or incorrect assumptions will lead to inaccurate outputs.
- The model may be used incorrectly or inappropriately. Even a fundamentally sound model producing accurate outputs consistent with the design objective of the model may exhibit high model risk if it is misapplied or misused. Models by their nature are simplifications of reality, and real-world events may prove those simplifications inappropriate. This is even more of a concern if a model is used outside the environment for which it was designed. Banks may do this intentionally as they apply existing models to new products or markets, or inadvertently as market conditions or customer behavior changes. Decision makers need to understand the limitations of a model to avoid using it in ways that are not consistent with the original intent. Limitations come in part from weaknesses in the model due to its various shortcomings, approximations, and uncertainties. Limitations are also a consequence of assumptions underlying a model that may restrict the scope to a limited set of specific circumstances and situations.
Keywords: active management of model risk, effective model risk management,
model risk management,
algorithm in governance,
rigor in process,
information analysis,
model,
rigor,
risk
Português:
risco de modelo
US Guidance on
Model Risk Management (SR 11-7). Board of Governors of the Federal Reserve System, Office of the Comptroller of the Currency, 4 April 2011. Available from <
http://www.ilanet.com.br/portal/pub/Express/Model/sr1107a1.pdf >. access on 5 January
2023.
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GregorioIvanoff - 19 Jan 2023
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