Using scenario analysis to manage uncertainty in economic loss calculations

19 May 2020 by Paul Croft

In last week’s article, I suggested that multiple “but for” scenarios of economic loss are likely in situations where an intervening event occurs. The need for multiple scenarios is driven, largely, by the degree of uncertainty inherent in the forecasting exercise. In this article, I outline how uncertainty can be managed through scenario modelling. As lawyers reading this article, there may be some value in thinking about the potential implications this may have on case management, including the scope of instructions, defining the question(s) to be solved, and the nature of, and access to, the information required to support the claim.

Scenario analysis recognises that there is no single picture of the future, but rather, a range of possible outcomes. While each economic loss claim is different, Exhibit 1 summarises some of the steps that I typically incorporate into my approach for building predictive financial models. As I take a contingency approach to modelling economic loss, I use Exhibit 1 as a guide, rather than a checklist.

Exhibit 1


Build out the baseline “but for” position

  • Understand and test key value drivers, variables and internal & external relationships (for example, macro-economic data on customers, revenue growth, competitors)
  • Obtain relevant data, develop key assumptions and collate / source required supporting information

Develop potential “but for” scenarios

  • Use the “gap analysis” approach (as discussed in my previous article, “The impact of COVID-19 on developing “but for” scenarios of economic loss”) to describe and articulate potential “but for” scenarios
  • Incorporate systems thinking to capture consequential impacts
  • Evaluate scenarios and identify the most likely scenario(s)

Predictive analysis

  • Use multi-linear regression and time-series analysis to predict possible outcomes based on linear relationships between variables
  • Start simple; add complexity as required and understanding deepens

Baseline comparison

  • Compare scenario outcomes against baseline “but for” position

Sensitivity analysis

  • Assess sensitivity of outcomes to changes in individual core variables


While the thinking underlying Exhibit 1 has evolved over time, reflecting my experience and continued education, here are three lessons that particularly influenced my thinking.

I first used regression analysis to quantify the plaintiffs’ losses due to the 1997 Thredbo disaster. In that case, I built a single scenario of economic loss founded on the strong correlation between revenue and the quality of the ski season (duration, snowfall). While the mathematical logic was undisputed, I’ve since learned that providing an opinion built on sound mathematical logic is unlikely to be sufficient on its own: providing context (for example, against a baseline position) and a range of potential financial outcomes (such as, through sensitivity analysis) are among the tools I consider using to build stronger arguments and to provide the Court with information relevant to its decision-making process.

Fortunately, the uncertainty inherent in “but for” scenarios of economic loss is usually – but not always – bounded by a finite end date. While this allows us to reasonably predict losses by looking backward, the challenge becomes managing hindsight and associated cognitive biases that may be inclined to recast historical events. To manage that risk, I approach long held business “truths” with a healthy degree of scepticism. In another instance, management informed me that the business’s historical financial under-performance could be explained by adverse changes in certain KPIs and cost drivers. Initially, I spent time understanding the business and its data. Using multi-linear regression, I was able to demonstrate to management the flaws in their explanations, directing them instead, towards a different set of relationships that could be relied on to more accurately forecast cost outcomes.

And finally, I’ve learned to develop more robust predictive models by constantly iterating between predictive analysis and outcome assessment. This has two distinct benefits:

  1. From a “build” perspective, complexity can be added incrementally once the more basic, and simpler assumptions are incorporated, justified and the outcomes assessed for reasonableness, and
  2. More rounded and better articulated scenarios can be defined and considered, which should ideally assist with a more objective assessment of the likelihood and consequences of each particular scenario and reflect the range of possible ‘truths’ the Court may find as fact.

Need advice?

Early forensic accounting input may assist in developing the best legal strategy. We may be able to give a broad overview of the main issues after a brief review of available financials and an outline of the background to the matter. We aim to help, so please feel free to contact:

Paul Croft                                                                                   Jacqueline Woods                                               

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