What are the most common behavioral biases in budget forecasting?

1. Overconfidence bias: This occurs when individuals are overly confident in their ability to predict future outcomes, leading them to overestimate their forecasting accuracy. 2. Availability bias: This bias occurs when individuals place too much emphasis on readily available information. 3. Recency bias: This bias occurs when individuals place too much emphasis on recent events or trends when making budget forecasts, while ignoring longer-term historical data.

To mitigate it, rely on objective data, consider various scenarios and contingencies, and seek diverse perspectives. Most of the new entrants in the market specially aged between 18-28 experience this phenomenon because they are ready to take higher risk. For instance, in project planning, an optimistic bias might result in underestimating costs or timeframes, but by incorporating past data, considering worst-case scenarios, and consulting experts, a more realistic and reliable budget forecast can be achieved.

No company will ever forecast for failure or mediocrity, unless it's known beforehand that that's the inevitable course of the business. Having a good check & balance system in place is essential (multiple peer reviews and a comprehensively wide array of scenarios) to help ground optimism bias. To present the best scenario you must always also commit to volunteer the worst. FP&A is a decision making information/insights/analysis provider, and as such, all cards on the table should be guideline #1.

Having worked in a corporate environment with a managerial role one of the most common biases that I had to cope with was Groupthink bias. Groupthink bias: Occurs when a group prioritizes consensus and cohesion over critical thinking, leading to a tendency to conform and avoid dissent. In budget forecasting, this bias can result in a lack of diverse perspectives and an uncritical acceptance of assumptions. Group members may hesitate to challenge the prevailing opinion, hindering the identification of potential risks or alternative strategies. It's crucial to foster an environment that encourages independent thinking and constructive dissent to mitigate the negative impact of groupthink on budget forecasting accuracy.

I believe optimism bias is a good bias as it leads to thinking and acting positively. But you must not become overconfident and you must rely on data for decision-making.

Some more strategies include - Encourage a mindset that embraces curiosity and exploration to prevent undue reliance on the first piece of data. - Implement a structured decision-making process that includes regular checkpoints for reassessment. By doing this, individuals can break free from the effect and incorporate new information as it emerges. - Encourage the use of analogies from unrelated domains. By drawing parallels to different contexts, individuals can disrupt the anchoring bias and introduce fresh perspectives.

In my experience it is helpful to wipe the board clean every once in a while. Take a step back and look at how you would structure your work if you could start over tomorrow. This opens the floor to new ideas and ways of exceeding targets.

It's essential to steer clear of forming quick estimates with limited data. Employing a variety of sources and techniques to create estimates is equally important. Additionally, consistently and methodically revising your forecasts can play a significant role. Such approaches contribute to ensuring that your forecasts are more precise and are not overly swayed by an initial reference point that might have become outdated or incorrect.

It's like wearing rose-colored glasses – you cherry-pick data that confirms your gut feeling, ignoring red flags or alternative scenarios. This often leads to over-optimistic forecasts that set us up for failure and missed opportunities. The consequences are often brutal. Missed budgets erode trust, demoralize teams, and force painful corrections down the line. It's like flying a plane using only the rearview mirror – you might feel good initially, but the crash landing is inevitable.

Confirmation implies a preference for information that confirms pre-existing beliefs or decisions. At the same time, contradictory evidence is neglected.

Confirmation Bias is considered dangerous because even if you are doing the right thing your brain will create doubt and you will always seek other people's validation.

It's essential to engage with a variety of information and perspectives that challenge your thinking. Critically analyzing your hypotheses and assumptions is equally vital, along with acknowledging and rectifying your own mistakes and biases. Embracing these methods can lead to a more even-handed and impartial comprehension, steering clear of the instinctive inclination to perceive only what aligns with our desires or expectations.

It helps to know your own personality type and whether you will be more prone to confirmation bias. The more you can create a process that removes any emotion and has objective criteria, the more you can eliminate confirmation bias.

Hindsight bias should be avoided as it will affect logical decision-making because you will assume the previous results that occurred and you will not be able to take a logical decision.

Embracing a probabilistic mindset acknowledges that while hindsight might provide clarity, the future remains uncertain. Incorporating feedback loops within forecasting processes allows for continuous learning and refinement, helping in developing more resilient strategies that account for the inherent unpredictability of future events.

It is vital, documenting and examining your prediction process and its underlying reasons. Equally important is to objectively assess your accuracy and performance, and learn from both your achievements and mistakes. Engaging in these practices aids in fostering a more grounded viewpoint, recognizing that future events are frequently shaped by unexpected and uncertain elements, and are not as foreseeable as they might appear when looking back.

This is the psychological bias that leads people to want to stick with a course of action once they've embarked on it, even when the original decision was wrong.

To improve your forecasting process, consider the following steps: 1. Diversify your team and get a fresh perspective. 2. Review your existing forecasting techniques frequently. 3. Establish a clear framework and guidelines for collecting and processing data needed for the forecasting process. 4. Rotate staff frequently to avoid groupthink and encourage new ideas. 5. Seek input from cross-functional teams and consultants if required. 6. Train your AI to diverse data sets to avoid biases in the process and also have a human quality check.

Common behavioral biases in budget forecasting include over-optimism, where individuals tend to be excessively positive about future outcomes, anchoring, which involves relying too heavily on initial information, and confirmation bias, where people seek or interpret information that confirms pre-existing beliefs. Additionally, there’s the availability bias, where recent or vivid information disproportionately influences forecasts, and herding bias, where individuals follow the actions of others rather than making independent judgments.

I always find that people always assume that next year will have higher sales than current year without consideration of macro economic effects.