Annualized Loss Exposure (ALE) is the most recognized and focused result from quantitative analysis within the RiskLens platform. This post will help clarify for our customers (and those interested in the FAIR model) what it is and what it isn’t.
What it is
Annualized Loss Exposure is the key metric in the simplest form of how we communicate risk. Let’s dive deeper here…
The combination of both of these elements (not just a single one) is what we call Loss Exposure.
ALE is useful in many ways. It allows us to prioritize or compare separate risk issues which often have different frequencies and per-event impacts.
What it is not
Annualized Loss Exposure is not a prediction. Remember that FAIR is a probabilistic approach. Understanding the probability of something is not the same as prediction (ex. Think rolling dice).
Another reason why it’s not a prediction is that people often misinterpret it as, “I am going to lose X amount of dollars per year”. That isn’t necessarily correct. For instance, in the examples above, that would be a reasonable forecast in scenario A. For example scenario B, when a single loss event occurs, the organization would lose $800,000 per event – however it is only likely to occur once every 4 years. In both cases remember that ALE is an annualized value.
One other important thing about ALE – it is not just a single number. Monte Carlo simulations are one of the core methods applied when running a FAIR analysis. The result from these simulations is a result set that has a minimum, maximum, and thousands of results between. We can use that result set to compute Average, Most Likely, 10th percentile and 90th percentiles, etc. How do we represent this within the RiskLens platform today? See below:
To wrap-up, let me conclude by giving you a sneak peek into the follow-up post for next week: With the above visual and result set for ALE… which number should we focus on?