Alternative Data and Credit: The Business Dimension

Alternative methods of credit scoring, increasingly supporting the underwriting of loans to unbanked and underbanked consumers, are also being adopted for small and startup business applicants that have historically been considered too risky. Just as on the consumer side, market participants say that these innovations are stimulating competition while furthering the cause of financial inclusion.

The combination of alternative data and technology-powered analytics is “changing how credit risk is managed by providing more real-time, accurate insights into a business’s creditworthiness,” explains Clifford Rossi, professor-of-the-practice and executive-in-residence at the University of Maryland’s Robert H. Smith School of Business.

Data points from transaction activity and algorithms go “beyond the traditional credit score. This improves the accuracy and efficiency of credit decisions for lenders and expands access to credit for businesses that may have been overlooked or underserved by traditional scoring methods,” says Rossi, a longtime risk manager and GARP’s CRO Outlook columnist.

“These approaches aim to provide a more comprehensive view of creditworthiness, which can lead to more confident decisions, a wider borrower pool, and expanded access to credit,” states Edward Maslaveckas, CEO of Bud Financial, an AI-enabled transaction services company with a “data intelligence” specialty.

Clearing Away Obstacles

Traditional FICO-type scoring remains entrenched, including among institutions that are embracing newer approaches. But a growing number are leveraging nontraditional data in various forms – though more to date for consumer, and particularly mortgage, loans than for business credit, Rossi says.

Bud CEO Edward Maslaveckas

“We have found that alternative scoring methods are particularly useful for small businesses and startups that may not have a long credit history or substantial collateral,” Maslaveckas observes.

The data for alternative business scoring and insights into cash flow and financial health can generally be obtained from banking information or business filings, he says. Candidates otherwise might “have no lending alternatives.”

“These businesses often rely on alternative data points, such as online sales metrics, to demonstrate their creditworthiness,” the Bud chief executive adds. “Showing bank statements and corresponding cash flow analytics allows lenders to confidently provide them with credit they may otherwise have trouble securing, and with better terms.”

A persistent drawback – that traditional credit scores can be up to 90 days old and therefore stale – can be overcome with alternative data and analytical tools.

Data and Predictability

Barriers are falling where access to financing has been limited, says Emmanuel (Manu) Smadja, chairman, CEO and co-founder of MPOWER Financing, which lends to international and DACA (Deferred Action for Childhood Arrivals) student borrowers otherwise likely to be shut out. Although the customers, from more than 200 countries and attending more than 400 North American universities, are individuals, the alternative-data challenges encompass a diverse range of primarily overseas assets.

“MPOWER is the only student lender worldwide that looks at both U.S. and foreign credit data,” Smadja points out. “We focus on each applicant’s future career trajectory in making credit decisions. We think ‘thin file’ is a misnomer. There are tremendous amounts of data available on these students.”

MPOWER CEO Emmanuel Smadja

MPOWER brings into its analytics a student’s college and academic standing, major subject, years in the field and other factors for a predictive picture of future earning potential and financial obligations.

“This forward-thinking philosophy has enabled us to make sound credit decisions to thousands of students who were left out by traditional lenders,” Smadja says. “We recognize that our international students likely do not have a prior U.S. credit history, they may not yet have a lot of assets in their name, and they may not want to obligate a family member or close friend on their loan.”

AI and ML

Smadja sees MPOWER as “similar to other fintechs looking to solve challenges and remove obstacles to success in the global economy.”

The alternative scoring trend parallels that of disruptive technologies such as artificial intelligence and machine learning (ML), says Matt Skudera, president and chief operating officer of the Credit Research Foundation. Even traditional models enhanced with machine learning can yield decisions “in a nanosecond.”

Likewise, for MPOWER, AI and ML significantly accelerate data capture and application analysis.

As with other fast-emerging technologies, alternative scoring needs savvy management, and developing in-house skills is a work in progress.

No Time to Wait

“The individuals that support it, manage it and use it on a daily basis have a different skill set,” Skudera stresses. “These new roles require a data analytics background, whereas before, the skills could have been developed over time.”

Matt Skudera, Credit Research Foundation

At the same time that specialized skill sets are in demand, new scoring methods are being built into e-commerce practices. This in turn defines recruiting strategies.

“The field is changing so fast,” Skudera says. “The ability to use technology to drive your organization through things like dashboards, business intelligence, communicating electronically, and sustainability is just so important.”

“The next step in this journey,” Maslaveckas intones, “will involve continued innovation in using nontraditional data sources and machine learning algorithms to further improve the accuracy and efficiency of credit risk management and lending decisions. We will also continue to prioritize the ethical and responsible use of data in our approach to alternative scoring.”