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Auditing Location-Linked Bias in Credit Scoring

by Joseph Issa, Justin Issa
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

@article{ 10.5120/ijca2025925922,
author = { Joseph Issa, Justin Issa },
title = { Auditing Location-Linked Bias in Credit Scoring },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2025 },
volume = { 187 },
number = { 54 },
month = { Nov },
year = { 2025 },
issn = { 0975-8887 },
pages = { 30-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number54/auditing-location-linked-bias-in-credit-scoring/ },
doi = { 10.5120/ijca2025925922 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-11-18T21:10:47.348914+05:30
%A Joseph Issa
%A Justin Issa
%T Auditing Location-Linked Bias in Credit Scoring
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 54
%P 30-34
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

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.

References
  1. Campisi, N. (2021, July 8). From inherent racial bias to incorrect data-the problems with current credit scoring models. Forbes. Retrieved December 9, 2021, from https://www.forbes.com/advisor/credit-cards/from-inherent-racial-bias-to-incorrect-data-the-problems-with-current-credit-scoring-models/.
  2. Center for Microeconomic Data. Center for Microeconomic Data - Consumer Credit Panel: Frequently Asked Questions - FEDERAL RESERVE BANK of NEW YORK. (n.d.). Retrieved December 9, 2021, from https://www.newyorkfed.org/microeconomics/faq.
  3. Credit score statistics: Consumer credit statistics and credit score by ZIP code: Experian. Consumer. (n.d.). Retrieved December 9, 2021, from https://www.experian.com/consumer-information/premier-aggregated-credit-statistics.
  4. Fraser | Discover Economic History | St. Louis Fed. (n.d.). Retrieved December 9, 2021, from https://fraser.stlouisfed.org/files/docs/publications/FRB/1990s/frb_111991.pdf.
  5. Frey, W. H. (2019, September 9). Six maps that reveal America's expanding racial diversity. Brookings. Retrieved December 9, 2021, from https://www.brookings.edu/research/americas-racial-diversity-in-six-maps/.
  6. Gabecortes. (2021, January 25). This map shows the average credit score in every U.S. state. Grow from Acorns + CNBC. Retrieved December 9, 2021, from https://grow.acorns.com/average-credit-score-state-map/.
  7. Give me some credit. Kaggle. (n.d.). Retrieved December 9, 2021, from https://www.kaggle.com/c/GiveMeSomeCredit/data.
  8. Hayes, A. (2021, December 7). What is redlining? Investopedia. Retrieved December 9, 2021, from https://www.investopedia.com/terms/r/redlining.asp.
  9. How are FICO scores calculated? myFICO. (2021, October 27). Retrieved December 9, 2021, from https://www.myfico.com/credit-education/whats-in-your-credit-score.
  10. How it works. VantageScore. (n.d.). Retrieved December 9, 2021, from https://vantagescore.com/lenders/why-vantagescore/how-it-works.
  11. Mapping Inequality. Digital Scholarship Lab. (n.d.). Retrieved December 9, 2021, from https://dsl.richmond.edu/panorama/redlining/#loc=5/37.8/-97.9&text=intro.
  12. Singletary, M. (2020, October 16). Perspective | credit scores are supposed to be race-neutral. that's impossible. The Washington Post. Retrieved December 9, 2021, from https://www.washingtonpost.com/business/2020/10/16/how-race-affects-your-credit-score/.
  13. Team, T. S. E. (2021, September 22). 7 major life events that affect your taxes. The Official Blog of TaxSlayer. Retrieved December 11, 2021, from https://www.taxslayer.com/blog/life-events-affect-tax-return/.
  14. What your ZIP code says about your credit score: Fiscal tiger. Fiscal Tiger | Better Information. Better Finances. Better You. (2021, December 9). Retrieved December 9, 2021, from https://www.fiscaltiger.com/zip-code-affects-credit-score/.
  15. Wikimedia Foundation. (2021, December 4). Criticism of credit scoring systems in the United States. Wikipedia. Retrieved December 11, 2021, from https://en.wikipedia.org/wiki/Criticism_of_credit_scoring_systems_in_the_United_States.
Index Terms

Computer Science
Information Sciences

Keywords

Credit scoring bias Redlining Ghost variables Location-based discrimination Algorithmic fairness & data transparency