Life
is complicated, and we are busy. People
just don’t have the time or the energy to analyze everything. So, we employ “heuristics” or “rules of
thumb” to help us make judgments. While
these rules of thumb are often helpful, they also cause systematic biases which
adversely affect our decisions. This
happens to all of us at home and at work.
And
yes, if you work in any role related to credit underwriting, the chances are
that these heuristics affect your decisions and the decisions of your
coworkers. Here are a few of the most
common biases:
- Availability: Our judgments are affected by the ease of remembering or retrieving events, and more vivid or more recent events tend to be more memorable.
- Regression to the Mean: We assume that we can predict the future from the past, that this year’s cash flow will be similar to last year’s cash flow. However, any extreme performance is likely to regress to the mean.
- Confirmation
Bias: We tend to
selectively search for information which supports the conclusion which we
desire to reach (or which is located wherever we have found the information
previously).
- Overconfidence: People tend to be overconfident in the accuracy of their predictions.
- Hindsight
Bias: We assess the
quality of a decision based upon whether the outcome was good or bad, not by
whether the original decision-making process was sound.
The
Gambler’s Fallacy is another bias, and this one has been shown by academic research to impact credit decisions.
People
underestimate the likelihood of sequential streaks occurring by chance. We tend to believe that things will even
out. This is the Gambler’s Fallacy.
For
example, in roulette, if red has come up four times in a row, then people are
more likely to bet on black.
If
a Major League umpire has just called a strike, then he is slightly less likely
to call a strike on the next pitch (after controlling for the actual position
of the ball).
And,
a study of lenders in India found that when an officer approved a loan, he or
she was 8% more likely to decline the next loan application.
How
can you combat this bias? First, learn
about it. For an important decision,
develop an algorithm that helps you decide, and have another set of eyes
examine the outcome. The right types of
incentives can help too. In fact, the lending
study found that adverse effects were reduced when stronger
incentives for accuracy (versus the number of approvals) were implemented.
If you are interested in learning more, then please
contact me here. I have been researching
behavioral science for a few years along with reviewing actual loan approval
processes and decisions. I have lots of
interesting examples and solutions (with a focus on commercial loans).
I will begin formally presenting this
information later in the year, but in the meantime, I’d like to perform a
couple of free trial presentations. If your
institution is interested, then please contact me.
Links:
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