Saturday, August 12, 2017

US Army risk management - a process for deal with uncertainty


We have already focused on the US Marine Corps approach to risk management, (see "Risk Management the US Marine Corps - it is a process"), but they are not alone within the military with formalizing approaches to decision-making under uncertainty. The US Army addresses the issue with their own variation on the problem in their risk management manual. Again, there is a focus on process and disciplined which should be the focus for any risk management program. Clearly, a common theme is to have a strong risk process as a response to uncertainty. 

The US Army focuses on four main principles. These principles include integration and decision-making at the right level as well as continuous assessments and always weighting return to risk.
The US Army five step process breaks risk management into two major parts, assessment and management. You need to measure risks, take actions, and then evaluation in  a continuous loop. Feedback and reassessment is critical to the process. 

A risk assessment matrix does a good job of matching frequency of risks with the severity of the event. This combination determines the level of risk. This is an approach that may be helpful for many money managers. A threat-harm trade-off determines the level of risk for a portfolio. Looking at risk from this framework is a different perspective, albeit not inconsistent, from the classic return-volatility framework.


Another interesting matrix from the army is their criteria for effective controls. This table tries to match activity with risk controls which help define roles in an organization. These criteria can easily be mapped into the activities of a money management firm.

Asset management risk management seems to focus on the mapping of risk into a retun-volatility or VaR framework and spends less time on the process. Clearly, the Army has to focus less on countable events and more on uncertainty. Consequently, there may be more value on process than statistics.  

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