Our credit ecosystem is designed to help consumers access fair and affordable financial resources needed to live and achieve their goals, whether that’s buying a house, leasing a car, going to school, starting a business, etc. That’s made possible by providing lenders with information on consumers’ payment behavior to make sound lending decisions.
Unfortunately, there’s been a long history of health and wealth inequities, resulting in some consumers from disadvantaged communities being left without extensive credit histories and paying more for financial resources. Oliver Wyman research shows there are at least 28 million credit invisibles in the U.S., and another 21 million consumers who are unscorable by the credit scoring models most commonly used by lenders today.
While our industry has taken significant steps to broaden access to fair and affordable credit, including incorporating more non-debt related payment history into credit decisioning and increasing financial education, there are some barriers we still need to overcome. Most notable among them is making newer data sources, technology and advanced analytics accessible to more lenders during the decisioning process.
For decades, credit decisions were based on mainstream payment information, such as mortgage payments, credit cards, auto loans, etc. And more recently, non-debt related payment data, like rent, utilities, telecom and even video streaming services, have been factored into consumers’ credit history. The addition of this information onto consumer credit reports has fundamentally changed the game. These insights are helping lenders provide more credit where credit is due.
But the path to increased financial inclusion extends beyond the inclusion of non-debt related payments. Leveraging a broader set of Fair Credit Reporting Act (FCRA) regulated data types, such as trended credit data, expanded public records data (e.g. professional licensure information) and data on the repayment of short-term small dollar loans can help provide a more complete picture of a consumer’s overall financial situation.
Unfortunately, not every organization is resourced to manage the technology and analytical components necessary to integrate multiple data sources into a single output or credit score, while others lacking experience with these new sources of data may be concerned with compliance requirements. The process is complex, but there are tools available to make it more accessible for those not currently resourced to address those challenges.
The fact is credit scoring models are limited in effectiveness by the data they ingest. We found that by incorporating expanded FCRA regulated data and advanced analytics, credit scoring models can score 96% of American adults. This is a significant improvement when compared to the 81% of consumers who are scorable with conventional credit scores that rely only on mainstream credit data.
To drive meaningful change, the industry needs to make expanded data accessible to more lenders and provide easy access to composite credit scores.
Beyond just being the right thing to do, leveraging expanded data and advanced analytics means lenders can have a more comprehensive and fair view of a prospective borrower’s payment history and creditworthiness. More importantly, that opens the door for lenders to say “yes” to more deserving borrowers without adding risk.
The surest path forward to more people entering the credit ecosystem and increasing financial inclusion is through data. Many in the industry are relying on conventional credit scores that were established decades ago that are unable to incorporate the expanded data sets and advanced analytics available today. That’s a barrier we need to break.
The tools and insights are available today and now is the time to use them. Embracing expanded data sources gives greater visibility to the credit invisible and previously unscorable and helps us create a more inclusive credit economy.
Greg Wright is executive vice president and chief product officer for Experian Consumer Information Services.
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