CAMBRIDGE, Mass., March 2017––The loan application process collects and uses private, qualitative, and hard-to-verify information to facilitate better screening and monitoring of borrowers. This “soft” information is thought to reduce the likelihood of bad credit outcomes.
However, research by MIT Sloan School of Management Visiting Professor Maria Loumioti and her colleagues, Dennis Campbell and Regina Wittenberg-Moerman, shows that several kinds of cognitive biases can impede the effective processing and interpretation of this soft information, undermining the decision-making process.“Our study looked at whether there are some behavioral aspects that lead loan officers to sometimes make bad credit decisions. We found that that while soft information generally improves credit decisions, certain cognitive constraints actually counter that effect and can increase adverse outcomes,” says Loumioti.
Using the internal reporting system of a large federal credit union, the study identified four cognitive constraints that can affect loan officers’ judgment in interpreting soft information and impact lending outcomes. Those biases include: shared characteristics/peer perception, limited attention, prior experience, and learning over the credit cycle.
First, a common identity between loan officers and borrowers likely influences the loan officers’ interpretation of soft information, as they may favorably perceive the shared characteristics as a signal of trust and lower risk. Interestingly, gender is a relevant shared characteristic. They found that lending based on soft information by male loan officers to male borrowers leads to worse credit outcomes relative to when both parties are women or of different genders. “This is consistent with prior research showing that men are more likely to trust, and are more biased in favor of, other men,” notes Loumioti.
Within this category of shared characteristics, they also looked at the influence of small organizational networks of peer loan officers on how soft information is processed. They found that these ties adversely affect members’ decision making because members are influenced by peers’ interpretations and past experience.
Second, when loan officers have limited attention or are distracted, they are more likely to misinterpret soft information. This can happen when they are busy or before weekends and around national holidays.
Third, employees with prior non-banking experience had higher rates of misinterpreting soft information. This is due, says Loumioti, to their judgment being influenced by skills and habits acquired through their previous experience unrelated to loan underwriting. Loan officers with sales-related experience tended to have the worst credit outcomes, possibly because they pursue loan issuances without carefully processing the implications of soft information.
Finally, the researchers showed that loan officers’ interpretation of soft information can be influenced by the credit cycle. Lending based on soft information leads to worse credit outcomes during credit expansions. In comparison, loan officers learn significant insights about how best to process and judge soft information during credit downturns.
“Our findings suggest that while lending based on soft information generally improves lending outcomes, cognitive biases can lead to misinterpretation of this information, substantially diminishing its benefits,” says Loumioti. “Importantly, under these conditions, lending based on soft information can actually lead to worse future loan and borrower quality.”
Loumioti coauthored, “Making Sense of Soft Information: Interpretation Bias and Ex-post Lending Outcomes.”
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