Systemic discrimination is part of U.S. history, and we continue to be plagued by it. These biases drive income disparities and limit wealth creation. Today, individuals are speaking out about their experiences with racism and discrimination, and they are asking organizations to make systemic changes that combat bias. The call to act is loud and clear. Now is the time to root out bias, empower people through their voices, and transform negatives into positives.
Recently, we announced the PositivityTechⓇ intelligent platform’s Bias Index: an AI predictive model that identifies prejudice within customer and employee complaints and makes it possible for financial institutions to repair products or unjust practices. Using the PositivityTech platform’s Bias Index, we found that:
- Products like mortgage and auto loans are four times more likely to have biased practices, which may be attributed to these transactions being done “face-to-face.”
In fact, a recent New York Times article reveals how bias has excluded black families in Minneapolis from buying homes, and therefore, building wealth, sharing: “More recently, banks have been more likely to turn down black loan applicants… even after controlling for income and credit risk.”
- Alternatively, complaints about products with low face-to-face interactions, like virtual currency, debt collection, and credit reporting show much lower bias scores.
When there are face-to-face interactions, people may be more likely to bring biases into their decisions — whether they do so consciously or unconsciously.
As tensions due to social inequities boil over, the PositivityTech platform can help financial institutions turn their customer voices into intelligence and proactively weed out systemic discrimination. We believe that negative input can have a positive impact, and that together, we can flip the script on complaints and help institutions provide equal treatment to all customers irrespective of race, age, religion, gender, sexual orientation, military service, or citizenship.
If you are interested in taking a stand against bias, please get in touch with me at firstname.lastname@example.org.