Feb 16, 2022 Insights, Member Services
In 2017, 1.7 billion people, over a quarter of the population, were unbanked globally. A poll conducted by the Morning Consult in 2021 found that 10% of Americans are unbanked and 24% are underbanked. Financial inclusion is an important part of growing your customers and members and being able to better serve the unbanked and underbanked.
Approximately 28 million Americans have thin or no credit file at all, according to FICO. These Americans have a more difficult time getting a loan from a financial institution that uses credit history and traditional scoring methods to determine creditworthiness. The Consumer Financial Protection Bureau has responded by encouraging lenders to develop innovative means of increasing fair, equitable, non-discriminatory access to credit, particularly for those limited by their credit history or lack thereof.
Fintechs have taken the CFPB’s advice to heart and use alternative data and analytics solutions to offer credit to those without established credit or those with thin files. In addition to traditional data, fintechs are using consumer-permissioned data to expand their financial inclusivity to the “credit invisibles.” Using data from personal finance accounts that the individual has provided access to, as well as payment information from utility, phone and other companies, this method of determining creditworthiness can supplement traditional credit score analysis. This data offers a more comprehensive view of an individual’s financial state. There are many additional data factors offered by this information, such as insight on personal money management like the number of overdrafts, balance activity, and the amount and history of savings. These data give lenders a more detailed and robust view of how much credit an individual can manage and their overall financial health. And it’s how they’re taking business away from your bank or credit union.
Fintechs have also taken full advantage of the predictive abilities of machine learning and the abundance of digitized data available, and so can your institution with partners like Open Lending. The digitization of credit scoring made scoring traditional credit bureau data simple. Machines can evaluate complex patterns quickly and with minimal errors and greatly increased their predictive abilities.
Traditional lenders can take full advantage of the digitization of tax documents, pay stubs, school transcripts, telco data, and more by leveraging this data to make their automated engines even more predictive. Using this data, machines are more likely to consistently predict default risk than assessments by humans. Fintechs are embracing machine learning and use a vast wealth of digitized information to better serve the underbanked and offer loans to those that would have limited options based on traditional methods of creditworthiness determinations.
By partnering to leverage alternative data, lenders cannot only compete with fintechs and grow membership but are able to increase financial inclusion and better serve the underserved.
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