As digital lending continues to grow in size, companies are looking for ways to make their services more efficient and profitable to both lenders and borrowers. And they believe artificial intelligence and big data hold the key to the future of loans. Lenders traditionally make decisions based on a loan applicant's credit score, a three-digit number obtained from credit bureaus such as Experian and Equifax. Credit scores are calculated from data such as payment history, credit history length and credit line amounts. They're used to determine how likely applicants are to repay their debts and to calculate the interest rate of loans.
Mortgages are one of the rare financial beasts most people with the means have, yet few actually understand. A study by the Pew Charitable Trust found mortgages are the most common type of debt in America, held by 44 percent of all Americans with any type of debt. NerdWallet estimated the average borrower owes $176,222 in mortgage debt although statistics vary wildly by state and income level. A new generation of fintech startups is helping millennials navigate the punishing housing market, from SoFi mortgages to the tech-savvy broker firm Morty, which raised $3 million to launch its marketplace for transparent price comparison, TechCrunch reported. Even traditional players like Morgan Stanley are hopping on the high-tech train.
Credit scoring and approval rates changed substantially with the arrival of alternative lenders, mainly due to the adoption of new practices in collecting and analyzing potential borrower data. Alternative data has played its role in expanding horizons for financial institutions and for creating an opportunity to enter the financial sector fir technology startups and data-rich international companies. While social media, for example, as a source of data for creditworthiness assessment is still at a nascent stage, certain startups are already claiming to have incorporated information from social networks into their frameworks. In the quest to reinvent the way to assess consumer-related risk (as well as extend credit to unscored and questionable), startups were found more imaginative than traditional institutions. Alternative data requires alternative approach to data analytics, which wide adoption of machine learning and artificial intelligence brought.
Dave Girouard, the chief executive of the AI lending platform Upstart Holdings Inc. UPST, -2.51% in Silicon Valley, understood the worry. "The concern that the use of AI in credit decisioning could replicate or even amplify human bias is well-founded," he said in his testimony at the hearing. But Girouard, who co-founded Upstart in 2012, also said he had created the San Mateo, Calif.-based company to broaden access to affordable credit through "modern technology and data science." And he took aim at the shortcomings he sees in traditional credit scoring. The FICO score, introduced in 1989, has become "the default way banks judge a loan applicant," Girouard said in his testimony.
Financial institutions, overcoming some initial trepidation about privacy, are increasingly gauging consumers' creditworthiness by using phone-company data on mobile calling patterns and locations. The practice is tantalizing for lenders because it could help them reach some of the 2 billion people who don't have bank accounts. On the other hand, some of the phone data could open up the risk of being used to discriminate against potential borrowers. Phone carriers and banks have gained confidence in using mobile data for lending after seeing startups show preliminary success with the method in the past few years. Selling such data could become a more than $1 billion-a-year business for U.S. phone companies over the next decade, according to Crone Consulting LLC.