Experian empowers financial inclusion through Open Banking

Experian announced the launch of Cashflow Attributes, a solution to help expand fair and affordable access to credit, particularly for thin-file and credit invisible consumers.

Experian empowers Open Banking

With over 900 income, cashflow and affordability attributes, Cashflow Attributes insights are available now and allow lenders to seamlessly integrate banking data into their decision-making.

A consumer’s credit report is the most effective means to assess lending risk; however, nearly 106 million US consumers are unable to secure credit at mainstream rates either because they are credit invisible, unscoreable by conventional credit scores, or have a subprime or below credit score.[1]

Layering traditional credit report data with cashflow insights helps create a more detailed view of a consumer’s financial health and creditworthiness for lenders and can provide more opportunities for consumers.

Why it’s important:

Financial inclusion: 42% of adults lack a conventional credit score in a range that typically grants access to credit at standard rates[2]. Cashflow Attributes, which leverages checking and savings account information, offers a more comprehensive view of an individual’s financial profile. Using traditional credit data with lender-obtained cashflow information may unlock opportunities for consumers who may not have qualified if a lender was using traditional credit data on its own.
Enhanced predictive accuracy: While cashflow insights are predictive on their own, when viewed with traditional credit information from Experian and expanded Fair Credit Reporting Act data, Cashflow Attributes can boost predictive accuracy by up to 20%[3] ¾ allowing lenders to drive revenue growth while mitigating risk.
Consumer willingness: Experian’s research shows 71% of consumers are willing to share their banking information if it increases their chances of credit approval.[4]

“Supporting financial inclusion and creating an equitable path to credit is ingrained in our DNA,” says Scott Brown, group president Experian Financial and Marketing Services.

“We believe banking information holds untapped potential and that our new Cashflow Attributes represent an exciting step forward that can easily be integrated into lending decisions. As we look ahead, we will continue to leverage our core credit data, new data elements and our analytics expertise to unlock new opportunities for both consumers and businesses.”

How Cashflow Attributes works:

Lenders requesting Cashflow Attributes provide Experian with depersonalized transaction information from their existing customers or with consumer-permissioned account information from other banks.
Next, Experian, as a service provider to its lender clients, analyses and categorizes the information using its proprietary categorization model.
And in seconds, Experian delivers the transaction categories and predictive attributes back to the lender.

Experian’s proprietary categorization model can also be leveraged independently by lenders to gain deeper insights, drive more personalized experiences, and help improve financial management tools.


[1] https://www.experianplc.com/newsroom/press-releases/2022/experian-and-oliver-wyman-find-expanded-data-and-advanced-analytics-can-improve-access-to-credit-for-nearly-50-million-credit-invisible-and-unscoreable-americans

[2] https://www.experianplc.com/newsroom/press-releases/2022/experian-and-oliver-wyman-find-expanded-data-and-advanced-analytics-can-improve-access-to-credit-for-nearly-50-million-credit-invisible-and-unscoreable-americans


[3] Experian analysis based on GINI predictability. GINI coefficient measures income or wealth inequality within a population, with 0 indicating perfect equality and 1 indicating perfect inequality, reflecting predictive capability.


[4] Experian commissioned Atomik Research to conduct an online survey of 2,005 adults throughout the United States. The makeup of the sample is representative of the U.S. population based on national census data regarding demographic variables such as gender, age and geographical regions. The margin of error for the overall sample is +/- 2 percentage points with a confidence level of 95 percent. Fieldwork took place between March 17 and March 21, 2024.


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