Historical loss databases can be analyzed to identify trends and project future risk patterns. Risk teams use statistical modeling techniques to evaluate historical loss distributions, separating high-frequency, low-severity events from low-frequency, high-severity tail risks.
To evaluate potential loss exposures across different business segments, organizations combine distinct probability and impact models:
Expected Aggregate Loss = Expected Event Frequency * Expected Event Severity
Where:
- Expected Event Frequency = Calculated using a Poisson distribution based on the historical rate of incident occurrences over a given timeframe.
- Expected Event Severity = Calculated using a Log-Normal or Generalized Pareto distribution to model the potential financial impact curve of the events.
By combining these statistical distributions, organizations can run simulations to project potential loss scenarios. This data allows firms to assess the adequacy of their operational risk capital reserves, evaluate the financial return on control investments, and adjust risk appetite limits based on changing exposure patterns.
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