The Dangers of Behavioral Bias During Economic Tails
By Dan Price, CPA CFA, Trellance
We’re in a period of unprecedented uncertainty. Inflation and corresponding interest rate increases are higher than we’ve seen in this generation. Home values and mortgage payments relative to income levels are greater than what we saw during the Great Recession.
You could make the case that we’re in a period of “Tail Risk,” generally considered to be when outcomes are more than three standard deviations from the mean.
Economists like to assume things will “revert to the mean,” or go back to how they usually are. It’s a means of self-preservation. However, anchoring to historical results has the potential to be catastrophically wrong on the tail end of economic cycles. Booms and busts.
Anchoring is one of many behavioral biases that impact our judgment. Financial institutions face data quality and interpretation dilemmas daily, and these challenges are exacerbated by the current state of our economy.
For example, the last credit crisis was largely attributed to overleveraging and high loan-to-value (LTV) home equity loans. Because these are not seen as issues in 2022, most folks don’t expect the correction in the housing market to be as dramatic and ignore the potential that other factors could ultimately result in a similar or even greater outcome.
In this example, a skilled financial analyst could likely create an extremely compelling case for either side of that argument using the same data set.
Questions surrounding data quality increase the opportunity to introduce bias, and a lack of precedent makes the impact of that bias more difficult to quantify.
So how do we avoid biases in data driven decision making? We do so by striving for independence, leveraging experience, and validating and back-testing our conclusions.
Independence refers to the separation of the analytics department from parties that may have a financial interest in the results of those analytics. For example, if you ask the Director of Payments to evaluate your credit card portfolio, you’ll most likely be sacrificing some of the objectivity of that analysis, because their job is dependent on the success or failure of the very thing they are evaluating, the credit card portfolio.
Independence can be achieved most directly by working with a third party. It’s important to consider who’s interpreting the output and driving the story. Licensing software to drive internal conclusions brings a different level of independence as compared to conclusions reached by a third party.
The next best thing would be maintaining independence in fact, a state of mind that permits the provision of an opinion without being affected by influences that compromise professional judgment, through an internal culture of objectivity. Create quantifiable and measurable goals that reward long-term success as opposed to goals that can be manipulated through aggressive storytelling.
People learn by making mistakes. In pursuit of avoiding bias, there are few substitutes to the experience of having fallen victim to these biases previously. Experience also generally brings the ability to differentiate between what conclusions are a result of organic findings and what conclusions are more likely the result of low quality information (data).
There is often a disconnect between those with experience with the business outcomes and those with experience in the data. Collaboration and constructive criticism are both key here. Those with experience in business outcomes should trust the outcomes of their analytics while applying professional skepticism – questioning results that don’t pass the smell test.
Those with experience with data should avoid statements like “Well that’s what the data says,” and use the team’s feedback to verify the results of the analysis.
Validation, Testing and Improvements
There is a reason folks refer to it as the Analytics Journey. Analytics is not a destination. Even well thought out models often result in imperfect suggestions. The same level of independence, collaboration and experience used in developing your model should be put into the testing of its results.
Maintaining humility through this process will allow you to either revisit or fine tune the structure and assumptions used in your models to improve the reliability of your outcomes.
The saying “Past performance is not necessarily indicative of future results” is most true when the circumstances influencing those results are changing. Drawing reliable, unbiased data driven conclusions requires independence and experience. Maximizing your likelihood of a successful long-term data strategy requires the humility to use the feedback gathered through testing to improve as you move through your data journey.
Trellance is a leading technology partner for credit unions, delivering innovative technology solutions to help credit unions achieve more. Trellance’s comprehensive suite of loan portfolio analytic services for credit unions includes loan risk analysis, value-added data enrichment, and identifying unique environmental factors that affect credit union portfolios. As a tech partner, Trellance ensures that credit unions have access to the latest generation of fintech solutions, filled with powerful tools such as artificial intelligence and machine learning.