It’s an old axiom in the consulting business that risk assessment is one of the most (if not the most) important part of decision-making. Ironically, 99% of the clients I have worked with, start their decision-making process, with a financial analysis.
Finances are undoubtably important, but they come second in my book in decision-making analyses, simply because I consider them to be affected by the level of assumed risk. In other words, the finances related to a decision, are not an input in our decision, but rather, an output based on other inputs (one of which is the level of risk assumed). This is also one of the most common mistakes I find in financial models that I am asked to review: Things that are clearly dependent on other variables, are considered as inputs, thus reducing the reliability and clarity of the results.
So how would you assess risk? Well risk is nothing else than the result of two variables: probability * impact. This is what we have all been taught at the university. But is it still true?
I believe that risks are multidimensional, not two-dimensional as the commonly applied model above, suggests. This has always been the case of course. The reasons why the two-dimensional model remained popular over so many years, are, in my view, the lack of data and (inability to see the whole picture) and lack of need for a more precise calculation (partly due to the lack of data).
Nowadays however, as the interoperability and connectivity of the world has matured, I believe it is high time we examined risks on a multidimensional level, meaning that risk, isn’t just the result of probability * impact of our decision, but also of the risk arising from other decisions that we will need to make down the road until we reach our objective and of course, the risk arising from decisions that others may make as a reaction to our own actions. Sort of like a Monte Carlo analysis if you will. Only then can we have a truly holistic view of the consequences of our decisions and truly understand the level of risk that we are assuming.