Not all non-prime borrowers represent a poor credit risk for lenders. By using
Though consumers seeking credit come from all walks of life, not all appeal to prime lenders. Consider this household profile: A working married couple makes a total of $70,000 per year. Due to some early credit mistakes and medical expenses, each has a credit score below 600 and has been locked out of the prime lending stream. However, they’ve paid their rent, cell phone, and utility bills on time for several years, and always have a few hundred dollars in a joint checking account. Now with a child on the way, they need to make purchases beyond their modest budget.
This household is typical of an underserved consumer lending segment: 160 million Americans with no access to prime credit even though many have proven payment records. As of Q3 2017, the personal unsecured loan market transacted $112 billion; 30% of that amount was lent by non-traditional ‘fintechs’, many who cater to non-prime households. It’s a growing market: in 2010, these lenders represented a
Such non-prime households can be very risky to lend to, but when properly selected they can become loyal customers who will borrow and repay, driving down overall origination costs and potentially making them worth the effort to the lenders who invest in identifying and serving them.
Understanding the underserved
“These potential borrowers are not from any particular ethnic or socio-economic background—it’s more situational,” says Brian Biglin, chief credit officer of
Through its research arm,
Technology helps balance risk and opportunity
Non-prime lenders must balance their desire to maximize revenue, lend responsibly, and manage risk. To do this, they must use non-traditional data to find and underwrite customers who others cannot, with speed and efficiency. It’s a tall order for some: non-prime lending is a category mired in paper-based processing of leads and applications in a market where speed of approval is everything to an anxious applicant.
“Our credit-approval system is driven by data and analytics,” Biglin says, referring to his firm’s extensive use and scrutinization of alternative data—financial information that typically isn’t available through a traditional credit report. “For example, expats and millennials have cell phones, cable bills, utility bills, subscriptions. Yet, a good majority of what they pay on daily terms isn’t even present on their credit report—if they even have one.”
Risk assessment: Call in the tech wizards
Alternative data is part of a larger qualification and approval microcosm that can include several technologies, processes, and business partners. Elevate works with its internal data scientists to take streams of unstructured, alternative data—often thousands of alternative data points—and run them through its risk models. These models employ algorithms, fostered by advanced machine learning techniques, to score creditworthiness and regulatory compliance, including a prospect’s potential for default
Pulling this all together for Elevate is the
Technology also allows Elevate to tailor the user application experience based on specific risk factors, so higher risk applicants, with inconsistencies in their applications, will trigger additional verification steps, “The Provenir risk decisioning platform allows us to create customized risk-based experiences. We can use thousands of data elements in real time to ease the user experience and make decisions,” said Biglin.
Increasing speed grows business, improves service
Many lenders need months to complete this process, which causes a problem: by the time they’re ready to deploy a new model with their updated criteria, they’ve already identified still more potential updates. As a result, knowledge gaps can actually grow with each cycle deployed.
“With a decisioning process like ours, we need to make changes daily, sometimes multiple times per day. Unlike a traditional application stack, Provenir in the cloud lets our power business-users quickly create rules and update highly sophisticated risk models,” Biglin says. What’s more, this process is completed in hours or days.
The platform also helps Elevate build value by deepening customer relationships. When prospects get approved and become customers, they become part of the ongoing database and the lender can consult with that data again, as needed, to determine opportunities to help customers in the future. In this respect, Elevate’s risk-management platform rivals those of even many prime lenders, who often don’t or can’t maintain that customer knowledge over time.
Elevate’s digital operation can analyze thousands of applications using proven algorithms and machine learning and approve more applications in much less time than the competition. Behind this advantage is a rapid integration of alternative data sources. “That’s critical for our customer base,” Biglin says. “They need cash fast, not in seven days. You must be fully automated and get that money to them quickly.” Elevate’s commitment to speed recently won it a National Association of Commercial Finance Brokers award for Best Short-Term Lender in the U.K. “Right now, the Fed is working on same-day ACH for the U.S. market. When it’s available, we’ll offer it.”
Looking forward with technology
“Until now, our customers have been almost completely ignored by the banks in the wake of the financial crisis. To support these underserved borrowers, Elevate is fast-forwarding its risk-assessment approach,” continued Biglin. “Data science and the decisioning process are driving our success—and the more efficient it all becomes, the more we’re able to reduce marketing costs, while originating more customers and reducing defaults. Adding data scientists, data sources, and tech partners like
Join Elevate's VP of Data Science, Ken Schultz, for the webinar 'Expanding your market: Using data to reduce lending risk and increase approval rates'.
