Brian Reshefsky – CEO
Once the cornerstone of all lending decisions until the mid-eighties, cash flow underwriting is at a new tipping point thanks to machine learning and data analytics.
EDGE client data shows that, with cash flow analytics, personal lenders can get 6%-8% improvement in second looks in their existing sales funnel. This enhancement could not only be a boost to the bottom line but also a way to be more inclusive, offer more personalized banking, and improve efficiency in the underwriting process.
We Are Now at “Peak Credit”
Cashflow underwriting isn’t black magic. Before the advent of a credit score, underwriting largely involved manual assessment of a borrower's financial situation, focusing on current income, expenses, savings balances, and other financial behaviors to determine their ability to repay a loan. This method was straightforward but time-consuming and subject to human error and bias.
In the mid-1980s, Automated underwriting systems (AUS) enabled lenders to use standardized credit scores and large datasets to evaluate credit risk. This allowed lenders to make faster, data-driven decisions, reducing the reliance on subjective judgment. However, it also meant an individual's current financial situation or cash flow no longer were fully taken into consideration.
Today, lenders are squeezing as much out of that data as they can, but credit bureaus still can't accurately measure the 49 million people with thin credit files, the 28 million who have never had a credit file and an additional 21 million whose credit reports do not contain enough information to generate a reliable credit score. Often these people are young adults, gig economy workers, and new immigrants, all rapidly increasing demographics.
In addition, credit bureau data doesn't reflect an applicant’s current financial status. The data used by credit bureaus can be outdated, reflecting financial activities from up to two years ago. Cash flow data, by contrast, is real time. By looking at current income, savings balances and more, lenders can get a more complete picture of applicants and flesh out a more complete picture of thin-file applicants.
Cash Flow Underwriting is Ascendant
Just as the usefulness of credit bureau data is maxed out – cash flow analytics are ascending. The advent of open banking and advancements in machine learning have made it possible to process and interpret vast amounts of cash flow data efficiently and effectively.
Armed with up-to-date data, empirical evidence of a borrower's ability to pay, and ML-fueled insights, lenders appraise thin file applicants more accurately, offer more competitive rates, and consider applicants with low credit scores that might otherwise fall outside their credit box.
Cash flow data can be useful in automation as well. Income verification, for example, can now be completed in seconds or minutes instead of days. This not only speeds up the decision-making process but also enhances the customer experience, making it more likely for potential borrowers to complete their applications.
The Future is Now
As credit bureau data's usefulness peaks and potentially declines, cash flow analytics are on the rise. Lenders who embrace this shift stand to gain a competitive edge. They can better serve thin-file applicants and those with low credit scores who fall outside traditional credit models. With real-time data and machine learning insights, lenders can make more accurate, fair, and timely lending decisions.
The potential to improve sales funnels by 6%-8% is just the beginning. As technology continues to evolve, so too will the efficacy and precision of cash flow analytics, heralding a new era of fairer and more inclusive lending practices. For lenders, the message is clear: there is no time to waste. The future of underwriting is here.