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Budgeting & Forecasting-The Bias and Motivation in Financial Modeling-Part 2

Budgeting & Forecasting-The Bias and Motivation in Financial Modeling-Part 2

In Part 1, we learned that Finance & Accounting professionals, are not immune to cognitive missteps. We too can exhibit a number of biases. As an example, have you ever found yourself in these situations?

  • Confirmation bias – Intentional or unintentional desire to prove a hypothesis, assumption, or opinion. “Look for the data that supports your belief/idea.”
  • Assuming causation – Seeing illusory patterns when nothing exists. Randomness = a trend “I see a pattern or correlation”
  • Inconsistency – Applying modeling decisions in irregular, or unpredictable ways. Changing estimates from month to month for no documented or quantitative reason. “Let’s randomly change estimates month to month.”
  • Recency bias – Focusing on recent events and ignoring larger data set or older data (can drive survivorship “Recent events matter more than older data”
  • Availability bias – Relying SOLELY on data and information READILY available. Convincing yourself that, if you have to work to acquire additional data, the data is likely not relevant. “I only care about the most READILY available information.”
  • Anchoring – Giving initial data collected more weight than later collected data. “The initial data means more to me than later collected data.”

How can you analyze data and information that you have received from others for bias?

The short answer is…it is difficult. You will need the strong tools. We may suspect bias exists but fleshing it out can be tricky and politically dicey. Start by asking yourself…’What would I need to believe for this data to be true’? Carefully consider the source of the data. Try to think through the ‘other side’. Forcing beliefs out in the open can render the opposing side to confront their own bias. Creating owners of outcomes is an effective tool to shine light into the shadows of bias – when faced with actually performing to an outcome, many times biased inputs are scaled back or disappear entirely. Finally, look for ways to test with independent experts (Anyone without incentives, but possesses knowledge).

How can you mitigate your own biases when creating budgets, forecasts and models?

  • Centralized reporting, analytics, and budgeting tools to facilitate detailed budgeting components, change comparisons and visualizations.
  • Use established and set rules for how to assemble inputs and on how to make estimates and assumptions (including full documentation and demonstration on assumptions).
  • Validate against baselines, past periods, peer data, and alternate sources of information.
  • Be careful with a prescriptive estimation policy – consistently apply the same unit of measures, rounding policy, tolerance level, uncertainty level, the shelf life of estimates.
  • Seek guidance on when to use estimates and or assumptions – especially when facing insurmountable uncertainty.
  • Apply corroboration in an effective manner – support with evidence or authority.

Understanding the underlying strategic solution that the model reflects helps you to understand client behavior. Once you feel connected to the big picture, you can demonstrate a safe degree of flexibility that demonstrates thoughtful partnering but allows you to maintain confidence parameters. Your client may even accept scenarios (High, Low, Most Likely) as a tool to build trust and allow them to have a voice (and influence).

While accepting inputs from a client is important, one tool is to apply ‘confidence factors’ – especially in face of highly volatile history. On occasion – human ‘gut instinct’ can get it right. Allow for this (but also document as well). Also, the first output is rarely the final product. Share input logic with stakeholders. Be prepared to negotiate – be a voice of reason. Settle when roadblocks are foreseen, but again, document. Demonstrate homework in a calm, respective manner. Don’t be too easily influenced – accepting clearly biased information (blame if project or decision fails). Finally, do not view your role as a data taker – think on your feet, negotiate fairly, keep feedback poignant.

In closing, bias is normal and quite typical of human behavior in forecasting and projecting. The most important (and first) step is to maintain an awareness of the potential for it (and understand other’s motivations as you gather inputs into your models). Truly unbiased financial forecasts and models are likely impossible. Although all models have the potential to be wrong, some models can be useful. Financial forecasting and modeling is a heuristic process, and with experience, we become aware of our own bias habits, as well as others.

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