mudeliriski

Mudeliriski, literally model risk in Estonian, denotes the risk that the use of a mathematical or statistical model leads to incorrect decisions because the model fails to capture reality accurately. It arises when a model’s design, data inputs, or implementation produce biased, unstable, or outdated outputs, and the risk may intensify as the environment changes or as models are repurposed beyond their original scope.

Common sources include model misspecification (incorrect functional form or assumptions), data issues (inaccurate, incomplete, or non-stationary data), estimation error (uncertainty in parameter values), model drift (changing relationships over time), coding or implementation errors, and misuse by users who rely on outputs without appropriate understanding.

Impacts of mudeliriski appear in finance, insurance, and risk management, where it can affect pricing, valuation, risk measurement, capital adequacy, and decision making. Outputs that underestimate risk can lead to underpricing or insufficient reserves, while overestimations can cause overpricing or conservative strategies. The consequences can propagate through budgeting, strategic planning, and regulatory reporting.

Management and mitigation rely on a robust governance framework. Key elements include maintaining an inventory of models, independent validation or review, backtesting and stress testing, thorough documentation, version control, and clear escalation procedures. Controls often involve model risk limits, periodic recalibration, and explicit communication of model limitations to users.

Regulatory contexts commonly require model risk management as part of broader risk governance. Authorities emphasize independent validation, transparent documentation, ongoing monitoring, and accountability for model performance and change management.