The human brain’s job is not to analyze data or make complex decisions. Our mind’s primary job is to ensure we survive the present day and live to see another. It plays many tricks with the facts to get us to
tomorrow.
The brain’s tricks and systematic deviations from rationality are called
cognitive biases. Researchers have discovered that the human mind is prone to
over 100 such biases. These served us well when humanity’s only concern was to hunt, gather, and procreate. In today’s world, however, our cognitive system can lead to errors, especially when making complex decisions like managing our finances.
Most digital banking applications are not designed around human cognitive biases
and only appeal to the logical side of the brain. Consequently, they tend to deliver suboptimal user experiences and can lead to poor financial decisions.
Below are three examples of how banks can design digital banking applications to better align with cognitive biases.
1. Simplification
One of the biggest shortcomings of the human brain is its sheer
laziness. One quintessential example is a cognitive bias called the “paradox of choice.” When it comes to choosing from a large number of options, people often find themselves paralyzed and
unable to decide. Having too many options makes us doubtful of and measurably less happy with our decisions.
Digital banking can compensate for this cognitive bias by offering simple,
user-friendly experiences with limited options for customers. Another way of simplifying the decision-making environment involves
harnessing the power of defaults. Our tendency to stick with default choices, known appropriately as
default bias, can be leveraged in digital banking by pre-selecting the most appropriate options to encourage positive customer behavior—for example, making paperless statements a condition to opt out of rather than in to.
Thus, banks could reduce users’ cognitive load and help eliminate the paradox of choice, increase customer satisfaction, and improve decision making.
2. AI-Augmented Decision Making
Cognitive biases can significantly impact how we make financial decisions, such as our tendency to prioritize immediate gratification over long-term goals. This so-called
present bias leads to sub-optimal financial decisions like overspending and reliance on credit.
Banks could use algorithms and AI to detect these cognitive biases and augment human decision making by nudging customers toward more desirable behavior.
Uber, for example, employs the psychological trick of awarding badges to incentivize drivers to work longer hours without forcing them. Another example is Virgin Atlantic recommending
pilots to use less fuel, thus reducing costs substantially.
Digital banking could use AI-driven recommendations to counter biases, for example, by nudging and rewarding customers for setting up and regularly contributing to long-term goals such as retirement. Another way of countering the present bias is by alerting
customers with money-saving notifications and tips whenever they are on a trajectory to overspend.
Such strategies help establish good habits and enable long-term financial well-being.
3. Cognitively Intelligent Digital Banking
In digital banking, banks can leverage cognitive biases to influence customers toward more optimal choices. An example of that is the cognitive bias known as “loss
aversion”. Research shows that people experience about twice as much pain over losses than
pleasure over an equivalent amount of gain. Have you noticed that, when shopping on Amazon, you tend to make faster purchasing decisions when only a few items are left in stock and Amazon tells you to “order soon”? Amazon is employing loss aversion to its
advantage by implying you will lose out if you don’t act quickly.
In digital banking, loss aversion could be utilized to frame decisions to emphasize the losses of not using a product or service instead of focusing on the benefits, such as highlighting the potential losses incurred by not utilizing the optimal savings
or investment products. Thus, banks could influence customers to make more optimal choices.
Cognitive biases manifest in varying degrees, and some people might be more susceptible than others to
certain biases. By analyzing past decision patterns, banks could use data analytics and AI to understand the differences between customers’ susceptibilities to cognitive biases. This insight can help optimize digital user experiences on an individual level.
In the future, we might see cognitively intelligent digital banking applications using AI to automatically optimize digital experiences for individual cognitive bias profiles.
Conclusion
The human mind is fascinatingly complex but prone to irrational habits. To engineer truly outstanding digital experiences, banks will need to focus on human cognitive biases and behavioral science as much as on functional superiority.
Such prioritization will create connections that drive a more meaningful customer experience, improve decision making, and boost financial health, resulting in more profitable and loyal customers.