Credit Score Bias: How can it affect you?
Sofia Castillo
John Jay College of Criminal Justice
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Abstract
Whether you have good credit or bad credit, it can affect your life in many different ways. But in what ways exactly? A credit score is a three-digit number, typically between 300 and 850, designed to represent your credit risk, or the likelihood you will pay your bills on time. Credit scores are calculated using information in your credit reports, including your payment history, the amount of debt you have, and your credit history. Higher scores mean you have demonstrated responsible credit behavior in the past, which may make potential lenders and creditors more confident when evaluating a request for credit. Having low credit means indicates you’re a riskier borrower than someone with a better credit score. Those with higher credit scores generally receive more favorable credit terms, which may translate into lower payments and less paid in interest over the life of the account. But those with a low credit score will pay more in interest over time than they would if they had better credit and a better interest rate. The more you borrow, the more you’ll pay in interest. There are side effects to having bad credit. You pay higher interest rates on your credit cards and loans, applications for credit cards, loans and apartments may not get approved, you will have to pay security deposits on utilities, you can’t get a cell phone contract, you can get denied for a job, you face difficulties opening a business and it might be difficult to purchase a home and/or car.
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In society, people get judged based on many things such as their race, color, ethnicity, size, religion and sexual orientation. We see and hear about it every day whether it is in real life or on television. But imagine being judged for something as small as your credit score. Yes, credit score bias exists.
Different companies have targeted consumers in the past based on where they lived. For example, according to the source, Eveleth, R. (2019, June 13). Credit Scores Could Soon Get Even Creepier and More Biased. Retrieved from https://www.vice.com/en_us/article/zmpgp9/credit-scores-could-soon-get-even-creepier-and-more-biased., it states, “Early credit companies knew that impressionistic records were biased, and introduced a more quantitative score to try and combat the prejudices of credit reporters. In 1935, for example, the Federal Home Owners’ Loan Corporation created a map of Atlanta, showing neighborhoods where mortgage lending was ‘best,’ coded in green, compared to “hazardous,” coded in red. This solution, it turned out, codified the discrimination against minorities by credit companies. Neighborhoods coded red were almost exclusively those occupied by racial minorities. These scores contributed to what’s called ‘redlining,’ a systematic refusal by banks to make loans or locate branches in these ‘hazardous’ areas.” In the past, minorities were targeted based on where they lived. Companies still redline communities to this day. Redlining is very unfair to those that it affects because it makes it hard for them to try to rebuild their credit. Redlining occurs often when companies use “e-scores”. E-scores are made up of data such as our zip codes and internet surfing patterns and are used to determine our “creditworthiness”.
Unlike the actual credit scores they resemble, e-scores are arbitrary, unaccountable, unregulated and often unfair. According to a source, O’Neill, C. (2017). Collateral Damage. In Weapons of Math Destruction (pp. 141–160). Great Britian: Penguin., there are customer-targeting services that provide companies with insights about potential consumers based on their location, web browsing and purchasing patterns. For example, the text states, “A Virginia company called Neustar offers a prime example. Neustar provides customer targeting services for companies, including one that helps manage call center traffic. In a flash, this technology races through available data on callers and places them in a hierarchy. Those at the top are deemed to be more profitable prospects and are quickly funneled to a human operator. Those at the bottom either wait much longer or are dispatched into an outsourced overflow center, where they are handled largely by machines. Credit card companies such as Capital One carry out similar rapid-fire calculations as soon as someone shows up on their website. They can often access data on web browsing and purchasing patterns…” A company named Neustar collects data on different customers and sorts them based on what these customers have purchased or searched online. This data is also matched with real estate data. By doing so, they can make inferences on that person’s wealth. Once people are placed at the bottom, it is very hard to get them out of there. Because they have a low score, companies won’t take the time to meet with them.
Young entrepreneurs are a key source of employment growth in the United States. They already face a lot of bias because they are young and new to the business world. Just like the average person, entrepreneurs face a lot of racial bias with their credit score as well. According to Robb, A., & Robinson, D. (2018). Testing for racial bias in business credit scores. Small Business Economics, 50(3), 429-443., the text states, “Limited access to capital is an especially acute barrier to increased entrepreneurship among minority business founders… found higher loan application rejection rates among otherwise equivalent… minority-owned businesses attempting to borrow. …Black and Hispanic applicants pay higher interest rates on business loans than White borrowers with similar characteristics. In general, minority-owned firms experience loan denial probabilities and higher interest rates then do white-owned businesses even after taking into account differences in creditworthiness and other factors…” Based on their skin tone, minorities face bias. Black and Hispanic entrepreneurs pay more interest than White entrepreneurs.
Technological advances have altered the process in which banks initiate a loan. Credit scores can have a great impact on housing affordability. Customers with bad credit will have to pay higher mortgage rates. According to Nelson, A. (2010). Credit scores, race, and residential sorting. Journal of Policy Analysis and Management, 29(1), 39-68., credit scores influence residential sorting behavior in Southern California. For example, the text states, “Because black households have significantly lower average credit scores than similarly situated non-black households, the omission of credit scores particularly biases interaction estimates for black households… For example, there are significant positive interactions between household credit score and home value, as well as between credit score and the average district home value. This indicates that households with higher credit scores are able to purchase more expensive homes located in school districts with relatively expensive housing.” Based on your race, residential sorting models provide biased estimates. There are connections between credit score and home value depending on who lives there.
There is evidence that indicates that credit scores are predictive of future loan performance and suggests that scoring increases the accuracy of risk assessment. This benefits lenders but they can also benefit borrowers by expanding credit opportunities. According to Avery, R., Bostic, R., Calem, P., & Canner, G. (2000). Credit Scoring: Statistical Issues and Evidence from Credit‐Bureau Files. Real Estate Economics, 28(3), 523-547., credit scores are used as a primary screen for applicants seeking credit. For example, the text states, “Bureau scoring models are built on the premise that past performance in repaying debts is the best predictor of future performance. They are designed to (1) rank individuals on the basis of their relative creditworthiness and (2) quantify the likelihood that a given individual will default… Nearly all evaluations of bureau scoring models have focused on their ability to predict relative creditworthiness. Uniformly, this research has found that their powerful predictors of default this research has found that their powerful predictors of default and delinquency.” It has been researched that these models have been used to rank people based on their credit. These models determine a person’s credibility using their zip codes.
In conclusion, credit scores can have a huge impact on you. Whether you have good credit or bad credit, companies use your credit score to rank you on your likelihood that you will pay money back in time. This can be devastating for those with bad credit because no matter how hard you try to rebuild your credit, some companies won’t give you the chance. Scoring models use your purchase history, search history and your location to determine what kind of customer you will be without giving you a chance first.
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References
Nelson, A. (2010). Credit scores, race, and residential sorting. Journal of Policy Analysis and Management, 29(1), 39-68.
Credit scores have a profound impact on home purchasing power and mortgage pricing, yet little is known about how credit scores influence households’ residential location decisions. This study estimates the effects of credit scores on residential sorting behavior using a novel mortgage industry data set combining household demographic, credit, and financial data with property location information and detailed community attribute data.
Robb, A., & Robinson, D. (2018). Testing for racial bias in business credit scores. Small Business Economics, 50(3), 429-443.
The article develops a novel empirical test of racial bias based on comparisons between forward-looking, expectations-based credit scores and backward-looking, repayment-history-based credit scores. Businesses founded by disadvantaged minorities have much lower average business credit scores, but these scores show no evidence of racial bias. If anything, forward-looking credit-score models under-predict the rate of payment delinquency among minority-owned businesses.
Swanton, Mary. (2010). Background bias: EEOC steps up pressure on employers that reject applicants based on criminal records and credit scores.(LABOR). InsideCounsel, 26.
Pope, D., & Sydnor, J. (2011). Implementing Anti-Discrimination Policies in Statistical Profiling Models. American Economic Journal: Economic Policy, 3(3), 206-231.
In many settings, factors such as race, gender, and age are prohibited. However, the use of variables that correlate with these omitted characteristics is often contentious. The article provides a framework to address these issues and propose a method that can eliminate proxy effects while maintaining predictive accuracy relative to an approach that restricts the use of contentious variables outright. The article illustrates the value of our proposed method using data from the Worker Profiling and Reemployment Services system.
O’Neil, C. (2016). Weapons of math destruction : How big data increases inequality and threatens democracy (First ed.).
Avery, R., Bostic, R., Calem, P., & Canner, G. (2000). Credit Scoring: Statistical Issues and Evidence from Credit‐Bureau Files. Real Estate Economics, 28(3), 523-547.
Although credit scoring offers benefits to lenders and borrowers, its use raises important statistical issues that may affect the ability of scoring systems to accurately quantify an individual’s credit risk. The evidence from a national sample of credit‐bureau records suggests that concerns about omitted‐variable bias may be justified, as local economic factors show significant correlations with credit scores.
