Criticism of credit scoring systems in the United States

Credit scoring systems in the United States have garnered considerable criticism from various media outlets, consumer law organizations, ^[1] government officials, ^[2] debtors unions, ^{[3] [4]} and academics. Racial bias, ^[5] discrimination against prospective employees, ^[6] discrimination against medical and student debt holders, ^[7] poor risk predictability, manipulation of credit scoring algorithms, ^[8] inaccurate reports, ^[9] and overall immorality are some of the concerns raised regarding the system. Danielle Citron and Frank Pasquale list three major flaws in the current credit-scoring system: ^[10]

1. Disparate impacts: The algorithms systematize biases that have been measured externally and are known to impact disadvantaged groups such as racial minorities and women. Because the algorithms are proprietary, they cannot be tested for built-in human bias.
2. Arbitrary: Research shows that there is substantial variation in scoring based on audits. Responsible financial behavior can be penalized.
3. Opacity: credit score technology is not transparent so consumers are unable to know why their credit scores are affected.

The scoring system has also been critiqued as a form of classification to shape an individual's life-chances—a form of economic inequality. ^[11] Since the 1980s, neoliberal economic policy has created an inverse correlation between the expansion of credit and a decline in social welfare—deregulation incentivizes financing for the consumption of goods and services that the welfare state would alternatively provide. ^[12] Credit scoring systems are seen as scheme to classify individuals creditworthiness necessitated by the loss of these collective social services. ^{[11] [13]} The credit scoring system in the United States has been compared to, and was the inspiration for, the Social Credit System in China. ^{[14] [15]}

The use of credit information in connection with applying for various types of insurance or in landlord background checks (for rental applications) has drawn similar amounts of scrutiny and criticism, because obtaining and maintaining employment, housing, transport, and insurance are among the basic functions of meaningful participation in modern society, ^[16] and in some cases (such as auto insurance) are mandated by law. ^[17]

Discriminatory effects

Credit scores are widely used as the basis for decisions to allow or deny individuals the opportunity to do things such as taking out loans, buy houses and cars, and open credit cards and other kinds of accounts. ^[18] This has been criticized as a practice having discriminatory effects. ^[19] Credit companies purport to measure creditworthiness by looking at information like: the number of accounts held, the age of associated credit accounts, consumer payment history of borrowed money, and the punctuality and consistency of payments.

As credit scores have become necessary to maintain credit and purchasing power, this system has been criticized as a wall between favored and disfavored classes of people. ^[11] The expansion of accessible credit can come with a downside of exclusion as people with poor credit (those that are considered high risk by credit scoring systems) become dependent on short-term alternatives such as licensed money lenders (the home credit industry), pawn brokers, payday lenders, and even loan sharks. ^[20] Credit scores can function as a form of social hierarchy that creates opportunities to exploit poor Americans. This can also prevent people from ever escaping their poverty or a poor financial past. ^[21]

Credit scoring systems also act as a way to treat individuals as objects that are subject to a particular set of quantifiable attributes. ^[22] In addition, they have a degrading potential that celebrates calculability over human needs. ^[23] Discriminatory responses to poor credit create a self-fulfilling prophecy as it raises costs for future financing which increases the likelihood of being unemployed or insolvent. ^[10] Since credit scores aim to classify people, other markets have expanded its applicability for use as a screening or assessment tool. ^[11] Credit is no longer used just for financial products such as mortgage loans, but is increasingly being applied cross-institutionally for other services such as:

• car insurance ^[17]
• health insurance ^[16]
• starting utilities (electricity, natural gas, water, etc.) ^{[24] [21]}
• employment ^{[5] [25]}
• rental housing ^[16]
• small purchase financing (e.g. cell phones, appliances, etc.)

Alternative credit scoring systems can use data such as rental payments, utility payments, subprime credit, and cell phone bills. ^[26] Other sources are social media activities, internet browsing history, employment history, student history, past loan application dates and locations, or the method one uses when purchasing gasoline. ^[27] Scores have also used for bespoke purposes such as dating. ^[28] Prior to the formation of the Fair, Isaac and Company (FICO) or the Fair Credit Reporting Act of 1970), early credit scoring systems such as the Retail Credit Company (now Equifax) in Atlanta, Georgia gathered information on individuals' sexual lives, disabilities, their political ideologies, and social behaviors. ^[21] Today, some scoring systems such as those developed by Versium Analytics are moving far beyond scores for financial products to measure probabilities that a consumer will commit fraud, cancel a subscription, be at risk of identity theft, buy environmentally friendly goods, donate to charity, among others. ^{[16] [29]}

Racism

Credit score systems are well known to contain racial bias and have been shown to increase racial disparities ^{[5] [30] [31] [32]} as studies show that African American and American Latino populations have substantially lower scores than the white American population on average. ^[1] Racial discrimination also results in impacts on the credit scores and economic security of communities of color—that ultimately, "entrenches and reinforces inequality by dictating a consumer's access to future opportunities". ^[1]

Numerous studies have found racial disparities in credit scoring:

• 1996 study found African-Americans were three times as likely to have FICO scores below 620 as whites and that Hispanics were twice as likely. ^[33]
• 1997 study found Black, Indigenous, and people of color [BIPOC] neighborhood consumers had lower credit scores. ^[34]
• 2004 study found high Black, Indigenous, and people of color [BIPOC] zip codes to have significantly worse scores than non-Black, Indigenous, and people of color [BIPOC] zip codes. ^[35]
• 2004 study found that African American and Hispanic consumers constituted over 60% of the consumers having the worst credit scores. ^[36]
• 2004 study found the median credit score for whites in 2001 was 738, but the median credit score for African Americans was 676 and for Hispanics was 670. ^[37]
• 2004 research study found fewer than 40% of consumers who lived in high-Black Indigenous and people of color [BIPOC] neighborhoods had credit scores of over 701. ^[38]
• 2006 studied US counties with high Black, Indigenous, and people of color [BIPOC] populations determining that those countries had lower average credit scores than predominantly white counties. ^[39]
• 2007 study by the Federal Trade Commission found that African Americans and Hispanics strongly overrepresented in the lowest scoring categories regarding auto insurance company's use of credit scores. ^[40]
• 2007 report found significant racial disparities in 300,000 credit files matched with Social Security records with African American scores being half that of white, non-Hispanics. ^[41]
• 2010 study found that African American in Illinois zip codes had scores of less than 620 at a rate of 54.2%. In zip codes that were majority Latino, 31.4% of individuals had a credit score of less than 620, and only 47.3% had credit scores greater than 700. ^[42]
• 2012 study examined the credit scores for about 200,000 consumers finding the median FICO score in majority minority zip codes was in the 34th percentile, while it was in the 52nd percentile for low minority zip codes. ^[43]

The outcomes for Black Americans because of this bias are higher interest rates on home loans and auto loans; longer loan terms; increased debt collection default lawsuits, and an increase in the use of predatory lenders. ^[44] FICO has defended the system stating that income, property, education, and employment are not evenly distributed across society and it is irrational to think an objective measure would not exhibit these discrepancies. ^[22] Tamara Nopper, sociologist at The Center for Critical Race & Digital Studies has stated that to solve the true issue of racism is not just to regulate it, as politics focus on, but to eliminate it in favor of public-owned banks that serve the community instead of shareholders. ^{[30] [45]}

A related concept of insurance scoring has also been shown to discriminate along racial lines, disproportionately harming black and Latino populations. ^[46]

Employment

Employers are unable to access credit scores on the credit reports sold for the purposes of employment screening but are able to acquire debt and payment history. ^[47] Credit reports are legal to use for employment screening in all states, although some have passed legislation limiting the practice to only certain positions. John Ulzheimer, president of The Ulzheimer Group and the founder of CreditExpertWitness.com, stated in a CNBC report that, "[credit scores] indicate if you're in financial distress. These are attributes that are important to employers. For example, would you want to hire someone in your accounting department who can't manage their own obligations?". ^[48] This approach has been noted as a discriminatory issue as the decisions can prevent one from gaining employment. ^{[5] [49]} Eric Rosenberg, director of state government relations for TransUnion, has also stated that there is no research that shows any statistical correlation between what's in somebody's credit report and their job performance or their likelihood to commit fraud. ^[6] The National Consumer Law Center (NCLC) has stated that credit scoring perpetuates economic inequality by controlling access to opportunities in the future as well as important necessities such as employment. ^[1]

In 2009, TransUnion representatives testified before the Connecticut legislature about their practice of marketing credit score reports to employers for use in the hiring process. Legislators in at least twelve states introduced bills, and three states have passed laws, to limit the use of credit check during the hiring process. ^[50]

Medical debt holders

Medical debt is often a barrier to obtaining credit, housing, and employment. ^[7] Because medical situations are often unexpected, they can cause an individual or family to experience financial distress, especially when unanticipated or "surprise" bills are unable to be paid. ^[7]

The debt is reported to credit bureaus due to payment delays, insurance disputes, confusion, or the dysfunctional nature of the US healthcare finance system. ^[7]

Credit scores treat medical debts the same as any other debts despite their involuntary nature (unlike opening a credit card for example). Some states have implemented laws to protect consumers against medical debts affecting their scores ranging from: ^[7]

• Prohibiting the reporting of medical debt for a certain time period after billing.
• Protections within payment plans for consumers.
• Restriction of reporting of medical debt for uninsured or underinsured patients or for patients that are negotiating disputes with their health insurance company.
• Requirements of notice when debt is reported.
• Protections focused on vulnerable patients (such as children).

The NCLC recommends eight key requirements for policy reform: 1) expansion of public financial assistance; 2) financial assistance minimum standards; 3) large health care facilities must screen for eligibility for insurance; 4) language assistance for understanding the financial process; 5) payments start after 90 days; 6) clarification of contractual violations for a hospital's forgiveness of a patient's copay, coinsurance, etc.; 7) protecting family members from a loved ones debts; and 8) enforcement of the statute through a private right of action. ^[7]

Student debt holders

The non-profit organization Student Debt Crisis along with Summer, a social impact startup that helps student debt holders published a national survey in 2018 that found 59% of respondents were prevented from making large purchases, 56% from buying a home, and 42% from buying a car. 58% reported that their credit scores had declined due to the debts, 28% were unable to start a business, 10% reported failing a credit check for a job prospect, and 13% failed a credit check for an apartment application. ^{[51] [52]} Rental application rejections and the inability to find sufficient housing is a well known consequence of credit scores as it leaves college graduates unable to participate in society. Even if loan payments are never late, debt-to-income ratios can be too high for landlords to approve an application. ^[53] Buying a home can be even more difficult, if not impossible, as student loans are often as big as or larger than an average mortgage. ^[54]

Inaccuracies and algorithmic subjectivity

Consumers in the US have very little control over how they are scored and even less ability to dispute unfair, biased, or inaccurate credit report assessments. ^[8] Scoring is automated, which results in potential consequences, often lacking oversight. ^[10] Credit reports by the three largest companies are commonly found to be incorrect with thousands of cases going to court each year. ^[9] Federal law requires agencies to investigate disputed information; however, "the agencies have operated for decades with systems that make it nearly impossible to conduct a comprehensive investigation, attorneys and consumer advocates say. The law is so nuanced, they say, that credit bureaus can essentially wash their hands of meaningful review." ^[9] In 2020, 280,000 complaints were filed to the CFPB regarding credit reporting error issues. ^[55] One of the alleged reasons for the excess of errors, according to Matt Litt, consumer campaign director with U.S. Public Interest Research Group, is that the credit reporting agencies are not incentivized to fix them because consumers are not the customers, but are instead the product—lenders, landlords, and other businesses seeking credit information are the customers. ^[9] CNBC reported that there is an "astounding number of errors in the credit reports that are the result of misaligned economic and legal incentives", ^[56] and a public poll by the Morning Consult indicated (74%) a demand for new laws or regulations to deal with credit bureaus. ^[57] CNBC proposed three solutions to the issue of inaccurate reports: ^[56]

• Liability for incorrect data must be changed as currently, there is no one held accountable and no penalties for not investigating disputes.
• Credit reports should be free and proactively available for consumers to monitor for inaccuracies.
• Expand the information usable by reports using big data.

A large percent of credit scores are estimated to have inaccuracies. ^[16] A portion of the inaccuracies stem from misattribution errors from the intermixing of data due to similar names or information. ^[58] Alternative data using personal data outside of the scope of traditional credit scoring is also known to contain inaccuracies. ^[59] Further, none of this data collection, the methods, or the parameters used to determine creditworthiness are public information. ^[60] Unfair judgements of creditworthiness creates an unfair and socially unjust ^[61] system that restricts participation in society. ^[16] These algorithmic inaccuracies driven by big data can have serious implications for human identity and status in society, a concept known as the "scored society". ^{[10] [16]}

Inaccuracy

Because a significant portion of the FICO score is determined by the ratio of credit used to credit available on credit card accounts, one way to increase the score is to increase the credit limits on one's credit card accounts. ^[62] This has been criticized as it acts as a way to incentivize accumulation of debts and deincentivizes people from financing purchases themselves through saving, ^[63] as well as normalizes the credit-debt system and consumerism. ^[64]

Credit invisibility

The concept of "credit invisibility" (a term used by the Consumer Financial Protection Bureau, the CFPB ^[65] ) is factored into this as there are many individuals who do not use or need credit (usually the elderly), avoid using credit, or avoid participating in the credit system. Being credit invisible puts consumers at a disadvantage. ^[26] Hispanic Americans are typically more likely to pay in cash and pool resources with extended family. None of this is visible to credit reporting agencies and therefore leaves Hispanics without the ability to make major purchases. ^[66] Another group of Americans that are, "left in digital poorhouse," a phrase coined by social scientist Virginia Eubanks, are young—in particular, millennials. This is due to access versus ownership—unable to purchase because of low credit, they seek alternatives to buying cars or houses. They also do not use credit cards as much as cash and rely on mobile payment apps like Venmo. None of these transactions are captured by credit reporting agencies and leave students' credit invisible. Further, millennials report believing that being debt free is a sign of financial success. ^[67] To build a credit score requires one to take on debt, acting effectively as a debt score. ^[68]

Alternative scoring systems

Credit invisibility combined with the rise of big data and artificial intelligence has given rise to a new market that challenges the traditional FICO model of credit scoring. ^[45] The use of alternative data has been pursued as a means to access more consumers, a form of market competition in an industry seeking greater profits. ^[45] Controversy exists regarding the invasive nature of the technology. Some of the issues are summarized here:

• Violation of due process may result as artificial intelligence scores may miscategorize consumers. Due process laws along with regulations based on this tradition must be used as a protective measure. ^[10]
• Credit scoring systems using AI lack transparency in decision making as the technology is patented. ^[10]
• Predictive algorithms run a high risk of being inaccurate and unfair, affecting peoples lives in discriminatory or arbitrary ways. ^[10]
• Alternative data collection can be invasive as it collects data beyond the scope of financial transactions (such as paying utility bills) to generate "digital characters" ^[69] based on social media accounts or internet browsing history. ^[45]
• Violation of consumer protection and fair lending laws (as well as human and civil rights violations) may result as privacy and security may be jeopardized. ^[16]
• Big data is attempting to address the issue of traditional credit scoring's inability to accurately predict risk, "credit invisible" populations, and "thin file" populations (people that have very limited or outdated credit histories). The aim is to build credit histories based on alternate information; however, it may result in lower scores instead of no scores (especially for people who are low-income) due to financial prioritization such as getting behind on utilities for high-cost months in favor of critical items. ^[59]
• Policymakers and regulators must focus on data accuracy, verifiable predictiveness, and the potential for discrimination. ^[59] Research strongly indicates none of these are being met by alternative credit scoring companies. ^{[59] [70] [71]}
• Redlining may return due to hidden biases in the algorithms. ^[72]
• The more data points used for a credit assessment, the greater the difficulty in transparency. ^{[8] [30]}

Poor predictor of risk

Credit scores are enhanced by having multiple credit cards, the use of credit cards, and having installment loans. However, financially secure individuals who do not use multiple credit cards, or who self-finance expenses, may be inaccurately assessed a lower credit score. ^[73] Some have blamed lenders for inappropriately approving loans for subprime applicants, despite signs that people with poor scores were at high risk for not repaying the loan. By not considering whether the person could afford the payments if they were to increase in the future, many of these loans may have put the borrowers at risk of default. ^[74] Some banks have reduced their reliance on FICO scoring. For example, Golden West Financial abandoned FICO scores for a more costly analysis of a potential borrower's assets and employment before giving a loan. ^[75]

Non-transparency

Credit scoring technologies are not public information as they are proprietary trade secrets of the companies that invent them. ^[10]

Regulation

Very little regulatory framework exists to ensure credit scoring algorithms are fair. ^[10] It has been suggested that scored individuals need to be granted rights for the various steps in the scoring process such as the method of data collection, how the score is calculated, to whom the score is disseminated, as well as how the score is used. ^[10] The Federal Trade Commission has also been targeted as the institution that should have greater regulatory oversight of the credit-scoring process as well as have access to credit-scoring systems to ensure fairness and accuracy. ^[10]

Ethics, morality, and inequality

Credit scores have been criticized as a systematic way to measure morality. ^{[16] [25] [11] [76]} They track consumption choices over time and so they are used to reflect a person's ability to manage money. The classification system of credit scores "rewards consumers who belong to the right category", and excludes those who are on the fringes of classification; credit scores nominally intended as a gauge of reliability as a lender becomes instead a gauge of morality. Companies keep records of purchasing behavior, which suggests certain behavior patterns, some of which are rewarded and others are punished—usually in ways that broaden the economic and (perceived) moral gaps between richer and poorer persons. These punishments can include higher premiums, loss of privileges, poorer service, or higher interest rates, which ultimately affect credit score and purchasing power. ^[11] This idea is similarly expressed with the Social Credit System in China as it acts as a tool to, [fix] moral decay" ^[14] and "encourage positive economic and moral behaviours". ^[16] The parallel between the two systems is that China's is outside of the market, while the United States' is within the market, so it goes noticed as an issue of morality. ^[76] Jonathan Cinnamon of the University of Exeter states the unfairness of credit scores and how they impeded our ability to function in society: ^[16]

Inability to secure a loan, mortgage, job, or health insurance due to inaccurate placement in a ‘risk’ category is clearly unfair, however the accuracy of the classification is perhaps unimportant in the context of social justice—accurate or not, personal scoring systems ‘make up people’ (Hacking 1999); they produce new social categories of difference and restrict our ability to shape our own sense of self, a clear threat to parity of participation in social life.

— Jonathan Cinnamon

Jackie Wang of the University of Southern California writes in Carceral Capitalism about how credit scores ultimately make moral judgments that increase inequality: ^[25]

Nowadays, credit scores have a number of often invisible effects on our lives. Credit scores (and even more dubious "e-scores" determined by private data mining companies) are often used for hiring purposes because employers believe that credit scores are a reliable way to index a person's level of responsibility. Yet considering that medical debt is the most common cause of bankruptcy in the United States, and that there are racialized structural barriers to accessing nonpredatory forms of credit, it is outrageous to use credit scores as a way to measure someone's personal character and make moralistic judgments about them. You could have a terrible credit score simply by being an uninsured black or brown person (without accumulated wealth) who gets into a bicycle accident. In short, using credit scores to punish poor people exacerbates already-existing socioeconomic inequalities.

— Jackie Wang

Marion Fourcade of the University of California Berkeley and Kieran Healy of Duke University discuss the concept of credit scoring as a tool for moral judgement, ^[76] übercapital, ^[76] as well as a form of class struggle. ^[11]

In the 1960s, there was a debate centered on the notion that ‘‘the poor pay more’’ (Caplovitz, 1963). With the Great Society and the expansion of welfare programs, it waned. But its main idea—that being poor costs money, that firms looking to do business with the poor know this, and systematically exploit it—is worth retooling for a neoliberal era. Debt has become more accessible, but also a lot more expensive at the bottom end of the social scale. And now it is not simply the ‘poor’ that pay more, but much more specific categories of people, measured and targeted by moralized market instruments and differentiated market institutions. Classification situations may have become the engine of modern class situations.

— Marion Fourcade and Kieran Healy

Frank Pasquale  [ pt ], a legal expert on artificial intelligence, algorithms, and machine learning and Danielle Citron of the University of Virginia School of Law contend that the algorithms used to decide credit scores need moral justification because of the large impact they can have on individuals. ^[10]

Predictive scoring may be an established feature of the Information Age, but it should not continue without check. Meaningful accountability is essential for predictive systems that sort people into "wheat" and "chaff," "employable" and "unemployable," "poor candidates" and "hire away," and "prime" and "subprime" borrowers. Procedural regularity is essential given the importance of predictive algorithms to people's life opportunities-to borrow money, work, travel, obtain housing, get into college, and far more. Scores can become self-fulfilling prophecies, creating the financial distress they claim merely to indicate. The act of designating someone as a likely credit risk (or bad hire, or reckless driver) raises the cost of future financing (or work, or insurance rates), increasing the likelihood of eventual insolvency or un-employability. When scoring systems have the potential to take a life of their own, contributing to or creating the situation they claim merely to predict, it becomes a normative matter, requiring moral justification and rationale.

— Frank Pasquale

See also

• Dodd–Frank Wall Street Reform and Consumer Protection Act
• Fair Credit Reporting Act
• Financial crisis of 2007–2008
• Financialization
• Right to explanation
• Subprime mortgage crisis

References

1. Past Imperfect: How Credit Scores and Other Analytics "Bake In" and Perpetuate Past Discrimination (PDF), May 2016
2. Democrats and Republicans in Congress agree: The system that determines credit scores is 'broken', February 27, 2019
3. Alone our debts are a burden. Together they make us powerful.
4. Collective Debt (June 23, 2020). Can't Pay, Won't Pay: The Case for Economic Disobedience and Debt Abolition. Haymarket Books. ISBN   978-1-64259-382-2.
5. Ludwig, Sarah (October 13, 2015), "Credit scores in America perpetuate racial injustice. Here's how", The Guardian
6. "Millions Need Not Apply". The New York Times , May 29, 2011.
7. Don't Add Insult to Injury: Medical Debt & Credit Reports (PDF), National Consumer Law Center, November 2019
8. Mikella Hurley and Julius Adebayo (2017), "Credit Scoring in the Era of Big Data", Yale Journal of Law and Technology, 18 (1)
9. Yamil Berard (February 19, 2019), "Credit errors upend lives of thousands of consumers", The Atlanta Journal-Constitution
10. Danielle Keats Citron and Frank A. Pasquale (2014), "The Scored Society: Due Process for Automated Predictions", Washington Law Review, 89 (1)
11. Marion Fourcade and Kieran Healy (2013), "Classification situations: Life-chances in the neoliberal era", Accounting, Organizations and Society, 38 (8): 559–572, doi:10.1016/j.aos.2013.11.002, hdl: 11858/00-001M-0000-0014-C967-4
12. Prasad, Monica (December 31, 2012), The Land of Too Much: American Abundance and the Paradox of Poverty, United States: Harvard University Press, pp. 233–235, ISBN   9780674067813 – via Google Books
13. Hacker, Jacob S. (October 9, 2006), The Great Risk Shift: The New Economic Insecurity and the Decline of the American Dream: The Assault on American Jobs, Families, Health Care and Retirement And How You Can Fight Back, United States: Oxford University Press, ISBN   9780195179507 – via Google Books^{[ page needed ]}
14. Karen Li Xan Wong and Amy Shields Dobson (2019), "We're just data: Exploring China's social credit system in relation to digital platform ratings cultures in Westernised democracies", Global Media and China, 4 (2): 220–232, doi:10.1177/2059436419856090, hdl: 20.500.11937/81128 , S2CID   197785198
15. Tom McGregor (March 2, 2019), Commentary: Actually, China's social credit system isn't the first, Channel News Asia
16. Jonathan Cinnamon (2017), "Social Injustice in Surveillance Capitalism", Surveillance & Society, 15 (5): 609–625, doi: 10.24908/ss.v15i5.6433 , hdl: 10871/30595
17. Legislatures, National Conference of State. "Use of Credit Information in Insurance 2011 Legislation" . Retrieved October 23, 2016.
18. "What is a Credit Score?".
19. [Rice, L and Swesnik, D: "Discriminatory Effects of Credit Scoring on Communities of Color" Suffolk University Law Review 46:935 (2013)]
20. Donncha Marron (2013), "Governing Poverty in a Neoliberal Age: New Labour and the Case of Financial Exclusion", New Political Economy, 18 (6): 785–810, doi:10.1080/13563467.2012.753043, S2CID   205779277
21. Sean Trainor (July 22, 2015), "The Long, Twisted History of Your Credit Score", Time
22. Donncha Marron (2007), "'Lending by numbers': credit scoring and the constitution of risk within American consumer credit", Economy and Society, 36 (1): 103–133, doi:10.1080/03085140601089846, S2CID   154718544
23. George Ritzer (1995), Expressing America: A Critique of the Global Credit Card Society, SAGE Publications, Inc., doi:10.4135/9781452243115, ISBN   9780803990449
24. Federal Trade Commission (May 2021), Getting Utility Services: Why Your Credit Matters, Federal Trade Commission
25. Jackie Wang (February 23, 2018), Carceral Capitalism, MIT Press, pp. 33–34, ISBN   9781635900354
26. Credit Invisibility and Alternative Data: Promises and Perils, National Consumer Law Center, July 2019
27. J. Merritt Melancon (May 17, 2022), How social media posts could affect credit scores, University of Georgia
28. Jessica Silver-Greenberg (December 25, 2012), "Perfect 10? Never Mind That. Ask Her for Her Credit Score.", The New York Times
29. Versium Predictive Scores and Datafinder, May 22, 2022
30. Rose Eveleth (June 13, 2019), "Credit Scores Could Soon Get Even Creepier and More Biased", Vice
31. How Algorithms Can Bring Down Minorities' Credit Scores
32. From Inherent Racial Bias to Incorrect Data—The Problems With Current Credit Scoring Models, February 26, 2021
33. Freddie Mac, Automated Underwriting: Making Mortgage Lending Simpler and Fairer for America's Families (Sept. 1996) http://www.housingfinance.org/uploads/Publicationsmanager/9706_Aut.pdf.
34. Fair, Isaac & Co., The Effectiveness of Scoring on Low-toModerate Income and High-Black Indigenous and people of color [BIPOC] Area Populations 22, Fig. 9 (Aug. 1997) http://market360online.com/sqlimages/1261/36693.pdf%5B%5D
35. Brent Kabler, Missouri Department of Insurance, Insurance-Based Credit Scores: Impact on Black Indigenous and people of color [BIPOC] and Low Income Populations in Missouri (Jan. 2004) https://insurance.mo.gov/reports/credscore.pdf
36. Report to the 79th Legislature--Use of Credit Information by Insurers in Texas (PDF). Texas Department of Insurance (Report). December 30, 2004. Retrieved January 29, 2022.
37. Bostic, Raphael W.; Calem, Paul S.; Wachter, Susan M. (February 2004). Hitting the Wall: Credit As an Impediment to Homeownership (PDF). Building Assets, Building Credit: A Symposium on Improving Financial Services in Low-Income Communities. Joint Center for Housing Studies of Harvard University .
38. Robert B. Avery, Paul S. Calem, & Glenn B. Canner, Credit Report Accuracy and Access to Credit, Federal Reserve Bulletin (Summer 2004) https://www.federalreserve.gov/pubs/bulletin/2004/summer04_credit.pdf.
39. Matt Fellowes, Brookings Inst., Credit Scores, Reports, and Getting Ahead in America 9-10 (May 2006) https://www.ciaonet.org/attachments/2800/uploads
40. Federal Trade Commission, CreditBased Insurance Scores: Impacts on Consumers of Automobile Insurance 3 (July 2007) https://www.ftc.gov/sites/default/files/documents/reports/credit-based-insurance5 scores-impacts-consumers-automobile-insurance-report-congress-federaltrade/p044804facta_report_credit-based_insurance_scores.pdf
41. Board of Governors of the Federal Reserve System, Report to the Congress on Credit Scoring and Its Effects on the Availability and Affordability of Credit 80-81 (Aug. 2007) http://www.federalreserve.gov/boarddocs/rptcongress/creditscore/creditscore.pdf
42. Sarah Duda & Geoff Smith, Woodstock Institute, Bridging the Gap: Credit Scores and Economic Opportunity in Illinois Communities of Color 8 (Sept. 2010), http://www.woodstockinst.org/sites/default/files/attachments/bridgingthegapcreditscore s_sept2010_smithduda.pdf
43. Consumer Financial Protection Bureau, Analysis of Differences Between Consumer- and Creditor-Purchased Credit Scores, at 18, Sept. 2012, http://files.consumerfinance.gov/f/201209_Analysis_Differences_Consumer_Credit.pdf
44. Credit scores are supposed to be race-neutral. That's impossible., October 16, 2020, archived from the original on October 18, 2020
45. Tamara K. Nopper (August 2020), Alternative Data and the Future of Credit Scoring (PDF), Data For Progress
46. Chi Chi Wu (June 2007), Credit Scoring and Insurance: Costing Consumers Billions and Perpetuating the Economic Racial Divide (PDF), National Consumer Law Center
47. Does checking your credit score lower it? Plus 12 other common credit score myths debunked, January 28, 2021
48. Can employers see your credit score? How to prepare for what they actually see when they run a credit check, August 27, 2020
49. There's No Such Thing as a Good Debtor, September 20, 2020
50. "As a Hiring Filter, Credit Checks Draw Questions", The New York Times, April 9, 2010.
51. Buried in Debt A national survey report on the state of student loan borrowers in 2018 (PDF), Summer & Student Debt Crisis, November 1, 2018
52. Diana Hembree (November 1, 2018), "New Report Finds Student Debt Burden Has 'Disastrous Domino Effect' On Millions Of Americans", Forbes
53. Natalie Kitroeff (June 6, 2014), "Young and in Debt in New York City", The New York Times
54. Annie Nova (April 19, 2018), Why buying a home can be almost impossible with massive student loan debt, CNBC
55. Ann Carrns (February 19, 2021), "More Consumers Complain About Errors on Their Credit Reports", The New York Times
56. Aaron Klein (September 27, 2017), The real problem with credit reports is the astounding number of errors, CNBC
57. Anna Gronewold (September 20, 2017), Poll: U.S. Consumers Back New Laws, Class-Action Suits After Equifax Hack, Morning Consult
58. Chi Wu (2009), Automated Injustice: How a Mechanized Dispute System Frustrates Consumers Seeking to Fix Errors in Their Credit Reports, National Consumer Law Center
59. Persis Yu, Jillian McLaughlin, and Marina Levy (2014), Big Data: A Big Disappointment for Scoring Consumer Credit Risk, National Consumer Law Center
60. Pam Dixon and Robert Gellman. 2014. The Scoring of America: How Secret Consumer Scores Threaten Your Privacy and Your Future. San Diego, CA: World Privacy Forum.
61. Hacking, Ian. 1999. Making Up People. In The Science Studies Reader, edited by Biagioli, M., 161-171. London: Routledge.
62. "Credit Scores: Not-So-Magic Numbers" Business Week , February 7, 2008.
63. Ana Kasparian and Jen Pan (June 5, 2021). "Weekends: Anti-Populism and the Wuhan Lab-Leak Hypothesis w/ Thomas Frank" (Podcast). Jacobin.
64. Lisa Peñaloza and Michelle Barnhart (2011), "Living U.S. Capitalism: The Normalization of Credit/Debt", Journal of Consumer Research, 38 (4): 743–762, doi: 10.1086/660116 , S2CID   145117956
65. The CFPB Office of Research (May 2015), Data Point: Credit Invisibles (PDF), Consumer Financial Protection Bureau
66. Kevin G. Hall (July 26, 2015), "Hispanics face hurdles in access to credit, mortgages", The Charlotte Observer
67. Christopher K. Odinet (2019), "The New Data of Student Debt" (PDF), 92 Southern California Law Review
68. Ramsey Solutions (April 28, 2021), UltraFICO: Ultra Ridiculous, Lampo Licensing, LLC.
69. Tamara K. Nopper (2019) "Digital Character in ‘The Scored Society’: FICO, Social Networks, and Competing Measurements of Creditworthiness;" Captivating Technology: Race, Carceral Technoscience, and Liberatory Imagination in Everyday Life, edited by Ruha Benjamin.
70. Ariel Nelson (December 2019), Broken Records Redux: How errors by criminal background check companies continue to harm consumers seeking jobs and housing (PDF), National Consumer Law Center
71. Kirsten E. Martin (2015), "Data Aggregators, Consumer Data, and Responsibility Online: Who is Tracking Consumers Online and Should They Stop", The Information Society, 32 (1): 51–63, doi:10.1080/01972243.2015.1107166, S2CID   205509140
72. Jordan Pearson (February 2, 2017), "AI Could Resurrect a Racist Housing Policy", Vice
73. "How Payment History Impacts Your Credit Score – myFICO". www.myfico.com.
74. Credit scores didn't fail in screening applicants for subprime loans Archived July 23, 2011, at the Wayback Machine (April 7, 2008)By Pamela Yip / The Dallas Morning News
75. "Credit Scores: Not-So-Magic Numbers" Business Week, February 7, 2008.
76. Marion Fourcade & Kieran Healy (2017), "Seeing like a market", Socio-Economic Review, 15 (1): 9–29, doi:10.1093/ser/mww033

Further reading

• Poon, Martha A. (2012). What Lenders See – A history of the Fair Isaac scorecard (Ph.D.). University of California, San Diego . Retrieved January 29, 2022.