Sally Taylor Forbes Councils Member
Forbes Business Development Council
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Leadership
B2B Scores VP and GM, leads business development, analytic R&D, product management, marketing and delivery for global FICO score products.
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There were 1.7 billion people who were unbanked in 2017 — over one-quarter of the current global population. In the U.S. alone, the Consumer Financial Protection Bureau found that 26 million people were “credit invisible” in 2010 because they didn’t have enough historical and recent credit experience for a traditional score. While the specific numbers and reasons differ from country to country, the one thing that is consistent is that credit, when consumers manage it properly, is a key to economic inclusion.
As we look ahead amid a changing economic landscape, three data trends may influence the lending landscape and provide lenders with more insight to help extend credit responsibly across the globe.
No. 1: Using Consumer-Permissioned Data To Gain A Better View Of Overall Financial Health
As the scores vice president at a company that offers alternative data and analytics solutions, I’ve seen that lenders typically rely on credit history and traditional scoring methods to determine creditworthiness. This can make it challenging for people without established credit, or those with thin files, to gain access to credit. In addition to traditional data, lenders are looking for more information to help improve credit decision-making. Moving forward, I expect to see more financial institutions using consumer-permissioned data. This means individuals provide access to the data from their personal financial accounts.
For example, a major credit bureau (paywall) plans to roll out the ability for banks and other lenders to offer customers the option to supplement their credit report with payment information from utility, phone and other companies. Other companies, such as LendingClub and Kabbage, use alternative data to facilitate peer-to-peer and small business lending. Major banks such as BBVA and HSBC are also leveraging open banking to allow customers to access financial products.
By offering access to data such as utility payments and banking statements, consumers can give lenders a current and more detailed view into their financial state and enable lenders to grant credit. By reviewing the length of the account, balance activity, responsible money management (such as a lack of overdrafts or bounced checks) and evidence of saving, lenders could gain more predictive power to understand how much credit the consumer can properly manage.
Credit scoring that leverages consumer-permissioned data has the potential to improve credit access for the majority of Americans. As a result, credit decisions could increasingly use consumer-permissioned data to provide a more robust view of an individual’s overall financial health.
No. 2: Leveraging Mobile Data To Better Serve The Underbanked
There are many countries where a large portion of the population is unbanked. This is often the case in developing countries where banking relationships are rarer and transactions are cash-based. People may use mobile devices to exchange money via SMS, similar to how they use Venmo. In these situations, there may be no infrastructure to support typical lending or microlending. Microlending is a way to facilitate economic inclusion and build financial resiliency for individuals in developing countries. Small, timely loans can jump-start or sustain income-generating activities, such as small, self-owned businesses.
The strongest example I’ve seen is in Sub-Saharan Africa. Payment platforms such as M-Pesa provide consumers with a digital wallet for Safaricom phones. Users can transfer money, purchase products and borrow funds from within the service.
In order to reach the underbanked with lending options, we will need more of these new, alternative business models. This may rely on partnerships between companies that own mobile data and lenders that provide the capital. Looking ahead, lenders will likely continue to try to find ways to incorporate alternative data into their credit decisions, particularly those focused on reaching underbanked populations. They could, for example, evaluate who a consumer sends money to and how often, and when to determine creditworthiness for microloans or other products.
Telecommunications companies may work directly with data providers such as credit bureaus to create interesting new business models such as mobile marketplaces that can provide lenders that decision insight. These new models could enable lenders to extend credit access to millions of consumers in key emerging markets who otherwise are credit invisible due to nonexistent, insufficient or inaccurate data in traditional credit bureau files.
No. 3: Using Machine Learning To Unlock The Predictive Power Of New Digitized Data
Before automated credit scoring, lenders relied on extensive written applications and reams of printed documentation, which was then reviewed by a human to determine if credit would be granted. It was a lengthy process and open to human error, and the information evaluated had to be kept very simple. Once the process was digitized, scoring traditional credit bureau data became easier, and more complex patterns could be evaluated, which increased predictive power. Machines were able to evaluate more complex data quickly, with fewer errors.
Today, more data is becoming digitized and available to determine creditworthiness. Now we have digitized tax documents, pay stubs, school transcripts, telco data and more. This data has the potential to improve credit scoring, making it even more predictive.
A recent report by McKinsey states that a Scandinavian bank reviewed its credit applications from the previous five years with its new decision engine. The bank found that the automated engine was more accurate and more consistent in predicting default risk than human assessments had been.
Artificial intelligence (AI) and machine learning (ML) are being embraced by fintechs. Boston Consulting Group estimated that “over $160 billion of digital loans were extended by fintechs in 2017 and by 2020, this number is likely to exceed $220 billion.” Access to these new digitized data sources allows improved algorithms to better evaluate credit risk. By coupling new digital data and ML techniques, lenders can have more confidence in their lending decisions.
While the economy continues to shift in the U.S. and globally, increased consumer credit score awareness and empowerment, combined with the greater availability of a variety of alternative data to improve decisioning, may help lenders, consumers and financial markets better manage the road ahead.
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