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Enterprise Risk Management And Handling Concentration Risk

Sean Salleh 09 Sep 2013 Risk and uncertainty

Concentration risk is a term that is used frequently in financial institutions to describe a risk that is significantly greater than others; for example, lending a high amount of money to a certain category of customers. The concept can be applied generally in enterprise risk management, as in the risk of developing and launching a new product, or the risk of moving a factory to another location. How much this kind of concentration risk is likely to influence the enterprise as a whole can then be checked by using a sensitivity analysis.

A global view helps to understand local peaks in risk Image source: en.wikipedia.org

How Concentration Risk Can Arise

One of the causes of concentration risk is, figuratively speaking, the left hand not knowing what the right hand is doing. In other words, separate business units in an enterprise engage in high-risk activities without knowing what their organizational neighbors are doing, and without any global appreciation of the levels of risk either. Stories of investment banks or stockbrokers falling apart when concentrated risk was identified too late are well-known: the rogue traders who were instrumental in bringing down their employers are one manifestation of this phenomenon.

Balanced Enterprise Risk Management

If an enterprise is to avoid unbalanced risk, its business units and departments should be speaking the same risk language, using similar techniques to model risk and above all communicating with each other. Any enterprise risk management policy that accomplishes these three initial actions has a significantly greater chance of benefitting a company as a whole and of identifying risks that could possibly destabilize it. It can also help to identify significant positive risks (ones that may generate large rewards) that may require special management and resource allocation to yield maximum positive benefit.

Risk Sub-models to Make the Overall Model

Using Analytica makes it simpler for different departments to make and maintain their own risk models. The influence diagrams in Analytica that users can create intuitively provide easy to understand information about how a model is constructed. The same diagrams then allow users to easily define and refine models without losing sight of the particular business goals of their department. Finally, the different business unit models or sub-models can then be combined as inputs into a model for the enterprise as a whole.

Sensitivity and Importance Analyses

The sensitivity in terms of enterprise risk management to the activities of any particular business unit or project can then be evaluated. An importance report can be generated to rank the different inputs in order of the influence they have on overall enterprise risk, and to show comparative risk impacts. Concentration risk will then show up as risks ranking as the top risk, plus the second biggest risk (if there is more than one concentration risk) and so on, but with significant differences to the other risks that follow in the rankings. These top-ranking, bigger than normal, concentration risks are the risks that may need to be better mitigated if they effectively threaten the future of the enterprise.

Combining Analytica Models is Simpler than Welding Spreadsheets Together

Analytica also demonstrates a number of advantages compared to spreadsheet applications that are also sometimes used for what-if type scenarios. Trying to combine spreadsheets is too often a time consuming and error-prone process, as is updating them to reflect changes in the situation. Analytica on the other hand is designed to make it easy to combine, extend and update business models, and therefore to regularly review and assess enterprise risk management policies.

If you’d like to know how Analytica, the modeling software from Lumina, can help you manage risks of all sorts, then try a thirty day free evaluation of Analytica to see what it can do for you.

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Sean Salleh

Sean Salleh is a data scientist with experience in guiding marketing strategy from building marketing mix models, forecasting models, scenario planning models, and algorithms. He is passionate about consumer technologies and resource management. He has master's degrees in Operations Research from University of California Irvine and Mathematics from Northeastern University.

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