Editor’s Note: BankBeat asked academics and marketers to weigh in on how behavioral economics is transforming the industry. This is part one of a two-part series.
Patterns of behavior, including how a customer feels — their emotional state when they are interacting with banking professionals — is valuable information in creating successful transactions and relationships. If bankers know the behavior of a group inside of its customer base in data-analytical terms, and know how their customers feel when they are interacting with their financial selves, it makes it easier to offer products and services that the customer may not know exist. This is behavioral economics applied to financial services.
Behavioral economics is a hybrid economic theory that combines social sciences and psychology, population statistics and decision theory to aggregate past consumer economic patterns of behavior to predict future behaviors and outcomes with real-world practical value.
The tenets of behavioral economics are practical and realistic and are, according to DePaul University economist Werner DeBondt, based on three premises:
- The notion that intuition is fragile.
- Regularities in behavior and decision-making processes are widely shared. Important among these regularities is “sentiment” and the way in which it is self-reinforcing. Sentiment matters and is not easily undone by sophistication and complexity. Analysis of behavior reveals the need, and when it’s known how a customer is feeling, the status of their emotional intelligence when they are making financial decisions, then we understand “sentiment.”
- That to be used productively, it has to engage the decision-making processes in a way that helps understand the “why” when decisions are made.
Behavioral economics is the analytical aggregation of individual behaviors and sentiments of people or organizations in the financial marketplace. It is a visual map of their actions, decisions and responses, converted into data to which analytics can be applied.
For example, neo-classical economic theory would predict that it is in a customer’s best interest to seek out a bank that has a branch in multiple, convenient locations. This presumption is based on the premise that customers actually want to conduct banking transactions at a branch with an in-person banking professional — relationship banking. It is convenient and full service, therefore in their best interest to obtain banking services at a branch.
Behavioral economics rejects the traditional process in favor of what younger customers refer to as frictionless: the smartphone in their pocket. The behavior that younger customers are responding to is the satisfaction found in conducting banking transactions immediately, without waiting in line, without driving to a branch, without wet signatures, without products they don’t need in the moment, without complex conditions, and without the investment in a personal relationship. In addition, millennials and members of Generation Z have an intense desire for fairness. Their perception of fair-play drives their behavior.
Fintech companies have identified these trends in customer behaviors and responded with products and systems that reflect the attitudes and behaviors of the aggregate customers. One customer rejecting the status-quo of branch banking would go unnoticed, however the discovery of an aggregate marketing preference coupled with appropriate technology tools launched an industry.
Behavioral economics is the mapping of the domain of human decision-making in well-defined behavioral settings. Behavioral economics does not reject classical and neo-classical economics; it ignores it.
Behavioral economics as a discipline acknowledges, without judgment, what people do, and how they feel about what they do, in order that valid predictions can be made with respect to both their current actions and future decisions. It uses applied statistics to elucidate, and the language and principles of the social sciences to explain how people behave. In a different way from neo-classical theory, modeling and prediction, behavioral economics simply uses a different set of tools.
When responding to the question whether classical and neo-classical economics are incomplete or obsolete, DeBondt responds in this way “…they are different tools…one is a hammer, and the other is a screwdriver…we need them both. [While I am] skeptical of classical and neo-classical economics alone…behavioral economics reveals other aspects and provides useful insights.”
Managing emotional states of mind can be complicated and uncomfortable: Consider how difficult it is to deny a loan to someone whose “family has been doing business with the bank for generations,” when that customer is unable to demonstrate the ability to repay. Equally difficult is how to initiate and nourish a relationship that results in the sale of products or services in an environment of fintech and electronic banking where a growing number of younger banking customers exhibit no desire to create the hallmark “personal relationship” with their local banker.
Behavioral analytical processes work backwards by relying on aggregate information to predict individual responses inside of groups or cohorts. Human decision making, meanwhile, is confounded by a plethora of biases, which are important to the behavioral economist, including:
Framing bias. Decisions can be influenced by the context in which a question is asked. For example, would you accept a risk if there was a 10 percent chance of failure or a 90 percent chance of success? Behavioral economics has proven that with equal risk, individuals choose 90 percent chance of success. Framing bias demonstrates that context matters.
Endowment effect. People are inclined to value an item already owned more than an offer for something new, which they must buy. Behavioral economics predicts that a farmer may overestimate the value of the farm that has been in the family for four generations yet they would not pay that price to buy a similar farm on the open market. Or, the consumer tendency to select lower quality when something is free (free is a distraction to natural decision making).
The law of small numbers. This is where a person’s limited experience (for the good or the bad) is used to form generalized conclusions, e.g. concluding that a market sector is unworthy of investment due to limited experience. A decision based on inadequate sample size is unlikely to fairly represent an aggregate market condition.
Arbitrary coherence. This is when a first decision or selection becomes justification for all future decisions or selections. A young customer who has a bad experience at a branch will likely generalize that experience to future interactions.
Fast thinking, slow thinking heuristic. Nobel Laureate economist Daniel Kahneman submits that people do not like, and are uncomfortable in situations where decision-making and selection requires concentration, calculation and analysis. The heuristic suggests people prefer to use intuition to decide and select “with their gut instinct.” Heuristic is investing in Fiat Chrysler because in the past, its engineering was outstanding and their new line-up has returned to classic lines, instead of examining forward P/E ratios, overvalue/undervalue assessments, earnings-to-debt ratios, or market predictions. Gut instinct is usually wrong, and as deBondt says, “intuition is fragile.”