Credit scoring for largely unbanked populations may sound like an impossible task. It is not. With the innovative thinking of some in the banking industry, the exploration and employment of new approaches may provide favourable resolutions. It isn’t an easy market to energise, but many in the unbanked population know and want the advantages that accrue by having a good credit score.
Banks and credit card companies are central to resolving the issue.
In a town with large minority populations, a local bank in the USA looked to ways to make the local citizenry valuable clients while boosting their credit scores. When the financial collapse occurred, many of the key problems became apparent. Focusing on the language barrier and the lack of financial literacy, the bank solved some of the problems. According to American Banker, “In 2007, … [the bank] opened a multicultural banking center in Brockton, where it provides free classes, including English as a second language, computer literacy, the basics of personal finance and home ownership…”
Payday lenders exist as the surrogate to traditional lenders and credit card companies.
Without a credit score or bank account, many people turn to payday lenders, enabling them to meet their weekly obligations — paying on the loans when they receive their weekly paychecks. The payday lenders have a notorious reputation likened to the loan sharks of generations past. “Perceived as unfair and even predatory, payday lenders have been targeted by regulators, consumer advocates and lawmakers who object to their pricing, which leaves borrowers in a debt spiral,” as observed in the American Banker.
The solution offered in this case is to combine loan resources. “Fintech firms and incumbents should collaborate on using alternative data sources to qualify more borrowers for bank-issued small-dollar loans,” as proffered in the above reference.
The other issue that serves to impeded the unbanked population’s ability to use traditional lending institutions is the data employed to determine creditworthiness.
Trust lies at the basis for determining credit score. Measurement of credit scores employs several well-known factors such as payment history, length of borrowing, timeliness etc. Many of the measures are out-of-reach of the unbanked population for several reasons. However, there is change that is proving effective. Citing the same article, “Fintech companies that lend to both businesses and individuals increasingly use alternative data sources and machine learning to gauge the likelihood that a borrower will repay.”
Machine learning stands at the forefront of this concept. With the ability to measure other factors of trust, which stand as the cornerstone of the lending institutions, reaching out to the unbanked becomes more probable. By the use of algorithms, the habits of individuals become a surrogate to the traditional measurements of creditworthiness. For example, do they take advantage of cheaper calling rates? Are they socially risky or inclined to visit certain types of sites? Do they gamble too much? Harvesting these factors through cell phones and social sites provides assurance that the borrower is a favorable client.
Changing the ways of business to attract and engage the unbanked population has taken creativity and effort, but banks are discovering that many are more than worth the risk.