Riskimalli

Riskimalli is a structured framework for quantifying and managing risk by estimating the likelihood and impact of uncertain events. It is used to support decision-making, prioritize mitigation actions, and inform allocation of capital or resources. A typical risk model links hazard identification, exposure, vulnerability, and consequences to produce a measure of risk, such as an expected loss or a probability of adverse outcomes.

Common elements include defining risk drivers, choosing a modelling approach, estimating parameters from data or expert judgment, and performing analyses such as simulations or scenario testing. Outputs often include risk scores, loss distributions, value-at-risk, expected shortfall, or probabilities of specific events. The modelling process may involve sensitivity analyses and stress tests to explore how results change under different assumptions.

Modeling approaches range from deterministic assessments to probabilistic and statistical methods. Techniques used include Monte Carlo simulation, Bayesian networks, fault tree analysis, event tree analysis, regression-based models, and, in some cases, machine learning. Data sources may comprise historical records, expert elicitation, sensor measurements, or transactional data. Validation and backtesting are important to assess accuracy and calibrate parameters.

Riskimallit are applied across sectors such as finance (credit and market risk, operational risk), insurance, project management, safety engineering, environmental and supply chain risk, and cybersecurity. They support risk governance, regulatory compliance, stress testing, and the design of mitigation strategies. Limitations include model risk, data quality issues, and evolving environments; robust practice relies on transparent assumptions, documentation, validation, and scenario analysis.