The figure cited most often is 200 million, approximately the number of adults across Latin America without formal credit access. Though broad and imprecise, it reflects a tangible reality. Consider a market in Brazil's Nordeste, a working-class area of Bogota, or the informal commerce corridors around Lima. Many people running businesses, supporting families, and managing monthly cash flow with care have no credit file. They have never borrowed formally and would be rejected if they applied tomorrow.
This is not mainly a story about poverty. Poverty and exclusion from credit overlap, but they are distinct. Many of the 200 million work and earn income. They pay utility bills, maintain mobile accounts, and use peer-to-peer financial networks. The issue is not that they cannot merit credit. Rather, credit assessment systems were not designed to observe the financial lives they actually lead.
Why the Credit Gap Is Structural
Bureau systems are built by collecting trade-line records from formal lenders. When a bank or finance company grants credit, the borrower's payment behavior, including nonpayment, enters the bureau record. Over time, the bureau creates a file available to subscribing lenders. This shared history lowers origination costs and supports the wider credit market.
The structural obstacle is the initial requirement. A bureau file requires prior borrowing from a formal lender. Formal borrowing, in turn, generally requires enough bureau history for approval. For first-time participants in formal finance and people whose finances have always moved through informal channels, this loop becomes a barrier. Financial education or willingness to repay alone cannot remove it.
Across Latin America, this barrier affects several major groups simultaneously. Informal workers, who account for 40% or more of the workforce in Brazil, Colombia, and Peru, often lack formal payroll records that lenders use with bureau information for secondary verification. Internal and international migrants arriving in a new place have no local financial footprint. Young adults entering work have no previous credit record by definition. These groups are excluded not because of risk, but because the bureau requires a data format they do not possess.
Variation by Country
Latin America's credit landscape is not uniform. The gap takes different forms across markets, as does the infrastructure available to address it.
Brazil has the region's strongest bureau infrastructure. Serasa Experian, SPC, and Cadastro Positivo, which as of 2019 lets lenders submit positive payment records as well as defaults, together reach much of the adult population. Coverage remains uneven, however. Urban, formally employed, higher-income Brazilians are well represented, while rural, informal, and younger people are not. Brazil's thin-file population alone is estimated at around 35 million adults, exceeding the adult population of many countries in the region.
In Colombia, Mexico, and Peru, bureau coverage is lower among every demographic, while informal employment is more prevalent. Their proportional unbanked credit gap is therefore larger. Mobile access is high and increasing, too. During the past five years, smartphone ownership has risen quickly in all three markets, supplying the precondition for alternative scoring where traditional bureau systems remain limited.
High mobile use alongside limited bureau coverage creates the clearest setting for alternative data scoring. Borrowers possess devices producing substantial behavioral data. What has been absent is the infrastructure capable of interpreting it.
Defining the Data Infrastructure Problem
The credit gap is often described as a financial literacy, banking distribution, or policy issue. Each view contains truth, yet they obscure the most direct technical account: this is a data problem. People excluded from formal credit are not absent from data. Their phone use, utility payments, peer transfers, and retail purchases produce records every day. The data is there. Missing is the layer that interprets it and converts it into a lender-usable assessment of creditworthiness.
The distinction guides solution design. Education programs address financial literacy. Branch growth or agent networks address banking distribution. A data problem calls for engineering: create pipelines for alternative data, models for scoring it, and APIs delivering scores to lenders during origination. These are manageable technical challenges. They need investment and repeated improvement, but engineering can solve them.
At Supersymm, we began with this framing: not "how do we persuade lenders to accept more risk," but "how do we give lenders data infrastructure to see risk they cannot currently see." The addressable population is behaviorally creditworthy. The task is measurement.
What Lenders Are Missing
For lenders, the credit gap is an addressable market that most institutions have yet to pursue. The thin-file population is substantial. In Brazil, the estimated 35 million adults lacking usable bureau files would represent, even at a conservative 20% annual credit-demand rate, millions of possible loan applications each year that are currently declined automatically.
Digital lenders and microfinance institutions testing alternative scoring repeatedly find that newly scorable thin-file groups are not uniformly risky. Like any applicant pool, they contain both genuinely high-risk borrowers and creditworthy people previously unseen by lenders. A scoring system creates value not by erasing that variation, but by measuring it accurately enough to distinguish the groups.
Lenders adopting this capability first may form the strongest thin-file customer relationships. Someone receiving a first formal loan from a lender able to view their alternative data may remain a long-term customer while building formal credit history. The advantage compounds because that borrower later creates bureau-format records visible to other lenders, but the original lending relationship is already in place.
A Realistic Timeline
Latin America's credit gap will not disappear quickly. The obstacles extend beyond technology. Access agreements with mobile carriers and utilities require regulations and commercial arrangements that take time. Models need continued validation and recalibration as economic conditions change. Lender integration takes months, not days. Those most in need are also least connected to digital platforms that produce the richest alternative data.
We take a realistic view of the timeline. From Sao Paulo, we are a small team addressing a large problem, not a major institution with continent-wide deployment capacity. Our contribution is the scoring layer, the infrastructure that converts alternative data into scores lender partners can use during origination. Broader inclusion depends on lenders, regulators, mobile operators, and borrowers moving in the same direction.
The data problem can be solved. Our part is making data readable in a format that genuinely supports lender decisions. The wider ecosystem must handle the remaining work.