Big Data is set to play a massive role in the rejuvenation of small business lending throughout the world. As traditional banks slowed their application approval rates following the financial crisis, alternative lenders took charge and provided a new outlet for SMEs to access working capital.
One of the biggest challenges for small business owners looking to borrow is their inability to convince a bank that they are financially responsible because they have little-to-no credit history. Now, alternative financers are utilizing Big Data analytics tactics to assess the creditworthiness of an entrepreneur from something other than a FICO score.
The use of data analytics, proponents say, allows borrowers to access loans they may not have otherwise been able to access. But industry experts are sounding the alarm on the process as one that is opaque and legally untested.
According to Louis Beryl, CEO and cofounder of alt-lending startup Earnest, using Big Data to assess creditworthiness is not exactly a new concept. “This is how underwriting used to be 100 years ago,” he said in an April interview with CreditCards.com. “They got to know people very deeply.”
The concept of aggregating personal data to assess credit risk has led to an explosion in so-called data brokers, who aggregate information coming in from an array of places, including more traditional sources like FICO scores and bank accounts, as well as less obvious sources like Facebook and other third-party sources of personal information.
In a recent column for economia, Validis chief executive Simon Leech explored the role of data analytics in boosting SME lending within the U.K. “So many of the improvements in the amount and the manner in which financial institutions lend to SMEs are dependent upon data,” he wrote, adding that accurate and up-to-date financial data allows lenders, traditional and alternative, to adequately assess the risk associated with a small business applicant. “This is the cornerstone to fostering a strong, diverse and competitive banking sector, ensuring that U.K. SMEs and the economy as a whole can benefit from high quality banking products and services at efficient prices,” Leech said.
Leech’s column is especially poignant within the current SME lending climate of the U.K., but his conclusions apply to lending technology and innovation throughout the world. “As technology facilitates greater data sharing in the field of SME finance, we will see not only different ways of working within SME finance but also new and different products, services and solutions which use the intelligence gleaned from this data to meet the specific market needs of SME businesses,” he said. “This will be a huge change for both the financial services industry and for SMEs themselves.”
According to Earnest’s Beryl, because traditional lenders do not often examine the type of data that stems from personal information about a borrower, an applicant isn’t getting credit for potential factors that would make them a qualified borrower. “That’s not fair,” he declared. The startup is one of several that have begun to look at personal information to determine how financially responsible someone is.
Indeed, other members of the lending community agree. Insikt CEO James Gutierrez, for example, touts the use of personal data as a way to put working capital in the hands of borrowers who previously could not prove through traditional means that they are financially responsible.
“We debunked that traditional view that they’re just a group of people here who are not creditworthy,” Gutierrez said in an earlier interview with PYMNTS. “That happened to be Hispanics, immigrants, who were hard working but without a credit score. And because they lacked a credit score, banks didn’t have enough data and declined them out of hand. With big data we found a way to approve them and have low losses.”
VantageScore is another innovative player that has grabbed the concept of Big Data analytics in credit risk assessment by the horns. “There are a fair number of nuances and complexities with using it,” said Sarah Davies, the Senior Vice President of Analytics, Product Management and Research at VantageScore in a discussion with PYMNTS earlier this month. “But I think, at the end of the day, incorporating other streams of data — like rent, utility, telco data — into more of these foundational models will ensure that everybody gets a credit file at the bureaus and can be accurately scored.”
Using Big Data to access detailed analyses of a small businesses’ likely ability to repay debts seems like a foolproof way of improving risk assessment. But the tactic isn’t without its caveats.
For applicants, their data may lead to their rejection but with no obvious reason. But with more traditional means of assessing creditworthiness, there is less confusion: a low credit score means less of a chance of getting approved for a loan.
Persis Yu, who works with the National Consumer Law Center, similarly highlighted the opaque process. “A lot of this data is untested,” he said to CreditCards.com. “If I miss a payment, I know that’s going to aversely affect my credit and my ability to get a loan further down the road.” But when algorithms and high-tech analysis are monitoring an array of data points to assess your creditworthiness, it is far less easy to predict and prepare for what will affect your ability to access a loan.
Not only do some opponents say that the use of Big Data analytics in lending makes the application process less transparent, but experts are sounding the alarm for the potential legal mess it could create down the road.
The New York Times pointed some of these out in an article from last January highlighting the potential for data-based risk assessments to violate regulations against discrimination in the loan approval process. It is illegal for lenders to discriminate against an applicant based on factors like race, religion and sex. “The danger,” NYT wrote, “is that with so much data and so much complexity, an automated system is in control. The software could end up discriminating against certain racial or ethnic groups without being programmed to do so.”
According to advocacy group PIRG’s consumer program director Ed Mierzwinski, the use of personalized data in assessing risk is precisely what many regulations in place today were designed to stop. The Fair Credit Reporting Act, CreditCards.com pointed out, was launched to prevent credit reporting agencies from unfairly accessing someone’s personal information. The lenders that use Big Data and that personal information to assess risk also prevent borrowers from benefiting from legal protections and rights that come with a credit report, Mierzwinski added.
The Consumer Financial Protection Bureau’s head of fair lending Patrice A. Ficklin told reporters that regulators do not want to stymie innovation, but there must be safeguards against unfair application approval processes. The Bureau, she said, is monitoring developments in Big Data creditworthiness assessments, so the lenders will be keen to similarly watch the responses by regulators to such novel lending methods. As with any game-changing innovation, only time will tell how the benefits and risks of Big Data in alternative lending will eventually balance out.
We’re always on the lookout for opportunities to partner with innovators and disruptors.
Learn More