Cash flow underwriting has so far proven to be an exciting alternative method to accurately predict an individual’s likelihood to repay a loan. But a few years ago using cash flow data for loan underwriting wouldn’t have been feasible for even one applicant, let alone hundreds. Now through open banking and machine learning, some individuals could gain access to lending opportunities for the first time. This post discusses some specifics about how cash flow underwriting can be accomplished through open banking.

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Who benefits from cash flow underwriting?

Everyone can benefit from cash flow underwriting, but especially individuals who have little to no credit history. Before cash flow underwriting, these individuals could not provide enough information to a credit reporting agency and therefore didn’t have any chance of being approved for a loan. However, cash flow data can fill in the missing pieces and provide legitimate data that accurately predicts that person’s credit default risk, even without traditional credit history. 

Lenders can also benefit as a result of cash flow underwriting, since it opens up an avenue for more individuals to open lines of credit with them.

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How could cash flow underwriting change lending as the industry moves forward?

As cash flow underwriting gains traction, we can see how it could eventually become the primary way lenders make decisions, at least for people who don’t have a very long credit history and for smaller loans. Traditional underwriting guidelines (especially for mortgages) have stayed the same for several decades, but these processes could change soon because of the potential for accuracy and inclusivity in cash flow underwriting.

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What is the connection between open banking and cash flow data?

Cash flow underwriting requires a lender to have access to an individual’s banking data in real time, (research shows most individuals are open to sharing that data to bolster their financial profiles). Open banking is the process through which an individual would give permission to share that banking data and for the underwriter to gain access to it. 

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How can cash flow data be accessed?

To start using cash flow data in underwriting, you need an open banking platform that will not only provide access to the data but also to an algorithm for analyzing the data to assess credit default risk. For example, one popular trend with cash flow analysis is to categorize all of an individual’s in and out money into major categories and then compute the percentile of the total that is allocated to each category. The idea is that the percentages (i.e., the distribution of one's money) gives a good insight on not just a person’s ability to repay a loan, but their actual likelihood to repay a loan. 

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If cash flow scores are so accurate, are traditional credit scores still necessary?

Right now, traditional credit scores remain relevant because they take long-term data into consideration whereas cash flow analysis generally only looks at the last 6-12 months (at maximum, 24 months) of a person’s banking data. Most likely cash flow analysis will become a significant component of the underwriting process, particularly for people with a low to mid-range credit score, but will continue to be paired with traditional credit data as well, if it’s available. Over the next few years, hopefully more research can be done to compare the accuracy of cash flow to traditional underwriting to confirm it’s reliable on its own.

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Learn more about underwriting and cashflow here and here.