An essential building block of consumer lending in the U.S. is the use of consumer credit data in the underwriting process for loan origination and portfolio management. These credit risk underwriting processes determine which consumers get access to credit and how much they can borrow and on what pricing and terms. But what if a consumer cannot meet the credit data conditions to gain access to credit?
The Credit Excluded Population
Currently, over 50 million Americans are considered credit excluded. They may be new to credit or haven’t chosen to use it yet because they don’t like the idea of using credit. But when they need to access credit, they often face a paradox in obtaining a credit score. To meet the conditions for accessing credit, consumers must already have credit. As a result, access to credit for the credit excluded is severely limited, if not fully prohibited, and any available credit access comes at a high financial price.
Historically, many lenders have assumed that the credit excluded do not have credit for various reasons — such as their income level, where they live or their lifestyle.
However, credit excluded consumers come from all walks of life. One example is younger consumers, who aren’t necessarily higher-risk borrowers. According to a recent Experian study, the risk levels for Gen Z consumers are 30% lower than the average of the overall U.S. population and more than 45% lower than Gen Y and Gen X Consumers.
Alternatives to Consumer Credit Data
Lenders have made efforts in the past decade to improve financial inclusion for the credit excluded through new data and analytics solutions. Still, few of the options offer a robust solution for the majority of the credit excluded. Here are some examples of potential solutions and their tradeoffs:
Analyzing rent, mobile, and utility payment data is widely recognized as offering solid predictive insight. However, the fragmented nature of the industries, coupled with diverse reporting standards, results in no definitive source and process for data aggregation.
Score developers such as VantageScore and FICO have developed a more precise risk assessment for consumers with sparse data and consequently have expanded the population of consumers they can score. Still, these enhanced analytics scores rely on core credit data from lenders, public records, and the like. So consumers without data at a credit bureau remain credit excluded.
So-called hybrid data analytics from companies like Experian and FICO have begun to include bank account data. Relevant data is incorporated into a conventional credit risk score via consumer opt-in, typically only given if including the data raises the score. Still, to benefit from the higher score, the consumer must engage with a lender who accepts these enhanced scores.
Why Cash Flow Data Offers Promise and Power
While no solution fits all, there may finally be a credible alternative to credit data-only risk assessment that can serve most credit excluded consumers. Consumer bank accounts are a tremendous source of history and insight into consumers' behaviors. According to the FDIC, approximately 93% of U.S. households have either a checking or savings account linked to them.
Pairing this information with the opportunity to access an up-to-the-minute read of the consumer’s transactional activity provides significant insight into the consumer’s employment and income patterns that are either unavailable or challenging to derive from credit data.
To learn more about the challenges of the credit excluded and why cash data is a compelling solution to more inclusive underwriting, download our new whitepaper.