Credit models today are failing. Lenders and borrowers are frustrated.
The problem is, lenders are going about things the wrong way. We’re all searching for the Holy Grail, but we’re being held back by adherence to an antiquated scoring system — FICO.
We’ve used FICO for decades. It’s simple, and everyone understands it as the key benchmark. However, the problem today is, it’s too simple. FICO is a very shallow, black-or-white look at an individual’s ability to repay a loan. Modern technology makes it possible to dive deeper into a borrower’s behavior and come up with far more predictive analytics.
The ability of today’s non-traditional lender to bypass banks altogether has certainly caused a disruption in consumer lending. Mid-risk lending options like peer to peer (P2P) depend on peer validation and often restrict access to capital based on a narrow collection of data. However, as these and other models continue to align with large financial institutions, they’re slowly moving back to the very model they intended to disrupt.
The next wave of disruption will be the ability to make loan decisions based on a multi-dimensional view of a borrower’s credit picture. This model enables the approval of more customers whose work histories, levels of education and other criteria indicate they are on their way up with improving credit profiles.
While last year’s release of FICO Score 9 was intended to better predict a consumer’s ability to repay a loan, the rating mechanism itself eliminates millions of responsible consumers who are ready and able to take on debt.
Still, with 90 percent of lenders using the FICO formula, most of the newfangled approaches to consumer lending lean too heavily on this system.
FICO provides a lender with a two-dimensional snapshot of a borrower. If that’s the best information a lender has, that’s what the lender has to use. However, we have the ability now to go much deeper and put big data into action.
For example, if an entrepreneur wants to open a restaurant, most banks would simply walk away because of the volatile nature of the restaurant industry. That’s a decision based on fear. To a certain extent, it may also be based upon ignorance – a lack of understanding of the specific opportunity, painting with too broad of a brush and determining all restaurant loans as too risky. But it’s possible to determine risk even in areas as unpredictable as the restaurant industry.
The challenge, of course, is the industry is constricted by FICO. Many lenders are finding out the hard way that not all 750s are the same. While many consumers are responsible and qualified, the use of antiquated and isolated credit variables prevents a large population of consumers from ever getting that chance.
Instead of relying on linear data, lenders should pay attention to consumers who are on their way down and on their way up. We all go through cycles at different stages of our lives. That’s why it’s important to look at the full scope of consumer data to make informed predictions on whether they’re getting stronger or weaker.
Unfortunately for the consumer who is having difficulties, traditional risk models will look for reasons to say, “No.” Given the data available to us today, a lender that uses technology to turn information into understanding is in a position to look for reasons to say “yes,” responsibly.
Innovation in technology, progressive big data analytics, biometrics, mobile payments and fresh thinking help us gain a better understanding of the gray areas of credit, so we can better identify risk and opportunity.
It’s time to move beyond traditional models and embrace big data analytics. It’s no longer about flipping a FICO coin to determine worthy borrowers. It’s about deploying smarter algorithms that can predict risk with laser precision. That’s where consumer lending is headed.
Tom Burnside is founder and CEO of LendingPoint. Juan E. Tavares is co-founder of LendingPoint.