Study Finds Women Face Higher Interest Rates Than Men When Applying for Credit
According to data from the Central Bank, women pay more interest than men when applying for credit for their own business. The average interest rate was 36.8% per year; for women it was 40.6%. In the state of São Paulo alone, there are 2.44 million female entrepreneurs, according to the Sebrae survey, who end up being disadvantaged due to historical characteristics, but which are not necessarily true.
Leandro Maciel, from the School of Economics, Administration, Accounting and Actuarial Science (FEA) at USP, explains that credit analysis is now automated: “You have a very high number of requests for credit. Since you have to process many requests, this type of processing ends up being automated in the sense that the decision to grant credit and the cost of credit will be determined by models that receive the information, process this information and then make this decision based on it.”
Historical problems
The problem with this is that the system has been fed with a biased history of women’s status. Since they historically earn less than men, this ends up being factored into the risk calculation: “Lower income affects the ability to pay debt, causing institutions that grant loans to consider them as higher risk agents and, therefore, end up charging higher interest rates and often not granting credit,” explains the professor.
When women had much less access to credit, and therefore less confidence in being good payers, interest rates for them tended to be higher. “We come from a structural condition where men traditionally manage family finances. If women have less of a track record, institutions generally end up not being able to infer what the main risk is for these agents and so end up granting less credit to women.”
Marks of the past
Today, however, the situation is no longer the same, or at least to a much lesser extent. The same is not true for the automated systems of financial institutions, which, when transposing old analyses, end up making assessments “without having the sensitivity to perceive that the world has changed, that women are more active, that their income has increased, that they are increasingly included in the formal job market,” says Maciel.
Because of this problem, researchers are already beginning to think about a more responsible use of the machine. The intention is to benefit from the agility and processing capacity it offers, but without repeating outdated analysis patterns. “The discussion is about not only providing this historical information to the models, but at the same time indicating a new structure for processing this information with the aim of achieving other types of analysis and trying to remove these behavioral biases that cause discrimination from the point of view of granting credit, that is, incorporating in these historical data the structural change that is not intuitive for the model to perceive.”
In addition to holding more executive and power positions, some research indicates that female CEOs tend to take more conscious risks, and are therefore more trustworthy when it comes to lending money. In practice, however, what credit ratings continue to show is that this group would be at greater risk due to a reflection of an outdated past.