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Modeling the Future: The Real-World Applications of Risk Analysis

15 Jun

In a world controlled by increasingly complex systems, it’s worth asking how anyone has any idea what is going to happen next. The field of risk analysis goes a long way towards answering that question. Sitting squarely at the intersection of statistical analysis and intuition, risk analysis is a broad discipline that can be approached from a variety of angles.

How Does Risk Analysis Work?

In business, risk analysis aims to answer two questions: how likely is it that a problem will occur, and exactly how bad will it be if it does? Risk analysts, in turn, approach these questions from two distinct angles.

  • Qualitative analysis. Qualitative risk analysis approaches a given situation holistically, using past outcomes and the personal experiences of the human actors involved to estimate the most likely outcome of a given situation. Farmers and professional sports teams often use qualitative analysis to estimate the likelihood of acceptable scenarios like break-even crop yields and sold-out home games.
  • Quantitative analysis. Thanks to the rise of complex computer-aided Monte Carlo simulations that use a wide range of input values to produce thousands of potential outcomes, quantitative approaches to risk analysis are more common these days. This form of risk analysis is used in fields from finance and resource extraction to law and supply chain management.

Forms of Quantitative Analysis

There are nearly a dozen accepted risk analysis models from which you can choose, each offering its own take on what’s coming.

  • Bell curve. The most common and easy-to-understand risk analysis model, this “normal” simulation merely asks you for values that describe the expected mean and standard deviation. The “top” of the bell represents the most likely outcome.
  • Lognormal. For values that can’t drop below zero, “lognormal” risk analysis models allow only for limited downside potential. As a result, the “mean” will be far closer to the expected minimum than to the expected maximum.
  • Discrete. This model is useful in binary or trinary situations, like court cases in which the three possible outcomes are “guilty,” “not guilty” or “mistrial.” It involves fewer inputs and does not necessitate the creation of complex graphs.

If you’ve owned or worked at the same business for a while, you may not need complex risk analysis models. In binary situations, you can perform a quick risk analysis by multiplying the expected probability of a negative event by its expected cost.

Recommended Qualifications

Risk analysis is no longer the sole province of number-crunching back-room rocket scientists, but it still requires experience and expertise. Your business will enjoy more consistent positive outcomes if you major in statistics, accounting, or business. If you’re enrolled in an MBA program or are considering earning one, you’ll certainly be required to take a risk analysis course.

As modern businesses continue to grow and the forces at work on them in the wider world become more complex, risk analysis has taken on new importance. If you’re worried about the future of your business, you can use powerful computer simulations to model the expected outcomes of certain situations. Armed with a mathematics or business degree and these sophisticated tools, you’ll be able to guide your business through thick and thin.

Michael Halpern is a freelance writer who blogs about career options for those with an MBA. If you would like to expand your career potential, he recommends that you get started with an online MBA program.

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