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Ten Common Behavioral Biases in Lending

There are 3 truths for any experience, your truth, my truth and what really happened. This is because we see the world through our own lens, filtered by bias. A behavioral bias is an irrational behavior or belief that influences our decision process. Understanding bias can you help you mitigate the negative impact of biases and make for clearer decision making.

1. Confirmation

Confirmation is the bias that you tend to look for and notice what confirms their beliefs, and to ignore or undervalue what contradicts their beliefs.

Example: An examiner is concerned of increasing risk within a portfolio. In response, a financial institution cites consistency in loans charged off, ignoring the rapid increase to delinquency

2. Representativeness

Representativeness is the bias that you tend to classify new information based on past experiences and classifications.

Example: A financial institution’s machine learning algorithm indicates that a reduction in recent borrower activity suggests likely member churn, ignoring that the change in circumstance surrounding COVID-19 may make this activity more likely to be an indicator of loss of income.

 3. Illusion of control

Illusion of control is the bias that you tend to believe that they can control or influence outcomes when, in fact, they cannot.

Example: Management at a financial institution rapidly changes portfolio composition in an effort to thwart increasing charge offs. Management has the illusion that their newest products are performing the best, when in reality losses are hidden within the growth of a new portfolio segment.

4. Anchoring and adjustment

Anchoring and adjustment is a psychological heuristic that influences the way people estimate probabilities.

Example: Everything about the allowance for loan loss calculation.

5. Mental accounting

Mental accounting is the bias that you treat one sum of money differently from another equal-sized sum based on which mental account the money is assigned to.

Example: Concentration policies that set limits for each portfolio segment separately as opposed to evaluating risk in its entirety.

6. Loss-aversion

Loss aversion is the bias that you tend to strongly prefer avoiding losses as opposed to achieving gains.

Example: A financial institutions tendency to make loans to borrowers with stronger credit quality ignoring risk premiums that are generally assigned to higher risk loans.

7. Overconfidence

Overconfidence is the bias that you demonstrate unwarranted faith in your own intuitive reasoning, judgments or cognitive abilities.

Example: Not acting on the results of your data analysis or avoiding analytics entirely.

8. Self-control

Self-control is the bias that you fail to act in pursuit of your long-term, overarching goals because of a lack of self-discipline.

Example: Current Expected Credit Losses (CECL) will drive the prevalence of this bias at financial institutions. Many will avoid growth in portfolio segments that will be most profitable long-term in order to avoid taking short-term hits to their allowance for credit losses.

9. Status quo

Status quo is the bias that you do nothing (i.e. maintain the “status quo”) instead of making a change.

Example: Avoiding digital transformation by saying things like “our members still prefer to come into a branch.”

10. Endowment

Endowment is the bias that you value an asset more when you hold rights to it than when they do not.

Example: Maintaining an inappropriate asset allocation by holding assets that you’re familiar with.

While understanding bias is important, knowing how to avoid biases in decision making can be more difficult to achieve. Creating processes and rules is one step in mitigating negative outcomes to bias. In our most recent article, we look at ways you can avoid biases in data driven decision making.

Dan Price, CPA, CFA

President

2020 Analytics

2O2O Analytics

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