Traps for the Unwary: Using Alternative Credit Data to Expand Credit Access to LMI Individuals and Underrepresented Communities

Traditional credit underwriting methods, which are generally based on credit reports, have not always successfully captured the full picture of a borrower’s ability to repay. It is estimated that more than 45 million US consumers lack sufficient credit history to generate either a credit report or a credit score.¹ Low- to moderate-income (LMI) individuals and underrepresented communities are disproportionally represented in that figure. Approximately 40% of low-income individuals and about 30% of moderate-income individuals have insufficient credit history to generate an accurate credit score.² Further, nearly 54% of Black Americans report having no credit or a poor to fair credit score, and roughly 41% of Hispanic Americans are in the same category.³  

Generally, when a consumer lacks a credit score, it is due to a lack of credit history or recent use of credit, meaning they may use cash, checks, or debit cards or have not taken out any loans with a traditional creditor. The lack of a credit score does not necessarily indicate that a consumer has financial issues or is a risk to lenders.

Some fintech companies are addressing the negative consequences of traditional credit scoring and underwriting by using “alternative credit data.” This type of data can be used to develop alternative scoring models to improve the assessment of a consumer’s creditworthiness, which has great potential to increase access to credit for LMI individuals and underrepresented communities. It is estimated that an additional 19 million US consumers could be accurately evaluated for credit using alternative credit data, which presents a significant new market for lenders and borrowers alike.⁴  

What Is Alternative Credit Data?

Alternative credit data is “any data that can be used to enhance consumer lending decisions, many of which are not traditionally included in the credit databases of the national credit reporting agencies.”⁵ Alternative credit data generally falls into one of two categories: “other financial data” and “nonfinancial data.” 

Examples of “other financial data” include conventional financial information such as rent, telecom, and utility payments, and bank account cash-flow data. Other financial data can also include data that can be ascertained from a consumer’s use of fintech services. For instance, a consumer may work for a ride-sharing company or food delivery service, cash out their payments to a debit card provided by a fintech company, and then use that debit card to pay for rent and utilities. A consumer may also use “buy now, pay later” (BNPL) services to purchase goods and services without having to open a credit account. Those transactions would not necessarily be reported to a credit bureau; however, they could serve as alternative credit data points and help to establish a consumer’s credit history.⁶ 

“Nonfinancial data” includes less conventional data such as educational and employment history, personality traits, relationships with friends and family, and digital footprints. Employment history could provide financial insights into a consumer’s income and their ability to make payments, while personality traits and relationship data may provide additional information about a consumer’s spending habits. Using a consumer’s digital footprint may also help confirm a borrower’s residential status and identity. 

Benefits of Alternative Credit Data 

Alternative credit data has the potential to “open the door to lower-interest credit options for those who are low and moderate income as well as those who have been underserved by traditional credit options.”⁷ Alternative credit data can provide lenders a more comprehensive picture of a borrower’s creditworthiness and help identify borrowers who would normally go unnoticed by traditional credit scoring models, which disproportionately affect LMI individuals and underrepresented communities. 

Studies suggest that using alternative credit data to assess a consumer’s creditworthiness results in a higher likelihood of a consumer being approved for credit with a lower interest rate.⁸ Alternative credit data, such as rent payments or other transactional activity, can be used to develop alternative credit scoring models in addition to or as a replacement for traditional credit scores. One study by FICO found credit scoring models based on alternative credit data are more powerful than traditional scoring models alone.⁹  

The Nexus Between Alternative Credit Data and Creditworthiness

Using alternative credit data with a significant nexus to creditworthiness is a good practice that can create highly consistent credit scoring models. However, before using alternative credit data, fintechs should consider the nexus between the data collected and creditworthiness. Not all forms of alternative credit data are as beneficial in determining a borrower’s creditworthiness as others. 

Some forms of other financial data, such as rent, utility, and telecom payments, have a significant nexus to creditworthiness.¹⁰ The connection between certain other alternative data and the likelihood of good payment performance is less clear. For example, some lenders have considered whether a consumer’s online social network includes individuals with poor credit histories.¹¹ This practice may raise concerns regarding the relevance to an individual consumer’s creditworthiness and the potential for discrimination against individuals living in underrepresented areas. 

Alternative credit data with a more speculative nexus to creditworthiness raises fairness concerns. The use of certain types of nonfinancial data could also heighten existing socioeconomic disparities in access to credit. For example, a consumer’s level of education can be used as a form of alternative credit data; however, this could result in discrimination against underrepresented communities and populations that have lower college graduation rates. It is important that alternative credit data be accurate, reliable, and representative of a broad range of consumers. 

Compliance Considerations When Using Alternative Credit Data

Fair lending is a central concern in any credit transaction. When using alternative credit data, it is important to consider whether the use of the data results in disparate impact. The definition of “credit transaction” has been interpreted to include fintechs that provide credit assessment tools or credit scores.¹² Companies may lower fair lending risk by ensuring that they test their systems and methods for potentially discriminatory classifications and disparate impact. 

Companies must also be vigilant about their collection, use, and sharing of alternative credit data. To comply with fair credit laws, companies should understand whether their use of alternative credit data could result in their classification as a consumer reporting agency, data furnisher, or a user of a consumer report. They should obtain explicit consent from consumers to use their alternative credit data, confirm that their use of the data is for a permissible purpose, ensure they can properly explain adverse actions resulting from the use of alternative credit data, and provide timely adverse action notices, when required. 

It is also important that companies consider unfair, deceptive, or abusive acts or practices (UDAAP) risks when using alternative credit data. Companies that wish to avoid UDAAP concerns should clearly represent to consumers the purpose for which they are collecting the alternative credit data, act according to their representations, and update their representations to maintain accuracy if business practices change.

Conclusion

Fintech companies are using alternative credit data to help improve credit records, offering consumers credit products that can assist in establishing credit histories, and using alternative credit data to facilitate the credit approval process. With the appropriate guardrails in place, these tools have amazing potential to expand credit access to LMI individuals and underrepresented communities. 

^[1] Federal Reserve Bank of Kansas City, “Give Me Some Credit!”: Using Alternative Data to Expand Credit Access (June 28, 2023).
^[2] Federal Reserve Bank of New York, The Role of Fintech in Unsecured Consumer Lending to Low- and Moderate-Income Individuals(November 2023).
^[3] CNBC, “Black and Hispanic Americans Often Have Lower Credit Scores—Here’s Why They’re Hit Harder” (Jan. 28, 2021).
^[4] Experian, “2022 State of Alternative Credit Data” (July 12, 2022), https://www.experian.com/blogs/insights/2022-state-of-alternative-credit-data-report/.
^[5] Federal Reserve Bank of New York, “The Role of Fintech in Unsecured Consumer Lending to Low- and Moderate-Income Individuals” (November 2023).
^[6] Federal Reserve Bank of Kansas City, “Give Me Some Credit!”: Using Alternative Data to Expand Credit Access (June 28, 2023).
^[7] Federal Reserve Bank of New York, “The Role of Fintech in Unsecured Consumer Lending to Low- and Moderate-Income Individuals” (November 2023).
^[8] Federal Reserve Bank of Kansas City, “Give Me Some Credit!”: Using Alternative Data to Expand Credit Access (June 28, 2023).
^[9] FICO, “Using Alternative Data in Credit Risk Modelling” (July 27, 2023).
^[10] For example, cellphone payment history can serve as a reliable and representative form of alternative credit data; it is reported that 97% of Americans own a mobile phone. Consumer Affairs, Cell Phone Statistics 2024 (Dec. 12, 2023).
^[11] Consumer Compliance Outlook, “Keeping Fintech Fair: Thinking About Fair Lending and UDAP Risks” (2017).
^[12] Reuters, “How Companies Can Manage the Risks in Handling Alternative Credit Data” (July 27, 2022).

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