| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 54 |
| Year of Publication: 2025 |
| Authors: Joseph Issa, Justin Issa |
10.5120/ijca2025925922
|
Joseph Issa, Justin Issa . Auditing Location-Linked Bias in Credit Scoring. International Journal of Computer Applications. 187, 54 ( Nov 2025), 30-34. DOI=10.5120/ijca2025925922
Credit scores are the gatekeepers to various benefits, including housing options, career opportunities, insurance costs, retirement funding, low-interest rates, loan obtainment, and more. Before the development of credit scores, prejudiced loan officers conducted face-to-face applications and used factors such as income, referrals, reputation, and character judgment to determine who received a loan. Then, in the 1950s, engineers William Fair and Earl Isaac invented the credit scoring models, in hopes of removing human bias from the equation for determining someone’s creditworthiness. These models, in the hands of credit companies that began collecting massive amounts of personal information from their customers, sparked public backlash over data privacy and discrimination. These public outcries eventually prompted the US government to intervene and enact laws that protected consumer rights. Specifically, the 1974 Equal Credit Opportunity Act barred credit companies and their models from using information like race, sex, marital status, religion, and national origin . Although the invention of credit scores was originally an attempt to eliminate bias, other factors can be included or excluded from these models that still put marginalized communities at a disadvantage.