Why rent-to-own providers and housing lenders are using behavioural data to assess repayment intent and expand access to home ownership.
Across Latin America, thousands of aspiring homeowners face the same challenge. They have stable income. They make their rent payments on time. They have worked hard to save towards a deposit. Yet when they apply for housing finance, they are often told they do not qualify.
In many cases, the issue is not affordability. It is a lack of traditional credit history. This creates a gap between people who are financially capable of becoming homeowners and the lending systems designed to assess them.
As new housing finance models emerge across the region, lenders are looking for better ways to understand risk. Increasingly, that means exploring alternative credit scoring approaches that can assess applicants with limited financial history. One of the fastest-growing areas is behavioural credit scoring.
Rent-to-own allows a customer to move into a property and build ownership over time through regular payments. A portion of each payment contributes towards eventual purchase, rather than being treated purely as rent.
This approach is gaining traction across LATAM for a few reasons.
Income is often informal or variable. Saving for a large deposit can take years. Traditional mortgage processes can feel slow and inaccessible. Rent-to-own offers a more practical path, especially for first-time buyers.
For lenders and platforms, it also opens up a much larger market. According to the Inter-American Development Bank, millions of households across the region remain underserved by formal housing finance. That gap represents both a social need and a commercial opportunity.
Most credit models are built on past financial behaviour. They rely on bureau data, repayment history and documented income. When that information is missing or thin, the model has very little to work with.
This leads to two common outcomes. Some applicants are declined simply because there is not enough data. Others are approved based on weak signals, which can increase risk later on.
For housing products, this becomes more challenging. Loan values are higher and repayment periods are longer. Getting the decision right at the start matters.
Behavioural data offers a different way to understand applicants. Instead of focusing only on past financial records, it looks at how people make decisions in the present.
Through short, mobile-based assessments, lenders can observe patterns linked to repayment. This includes consistency, planning, and responses to risk. These are not abstract traits. They show up in how someone approaches choices, manages trade-offs and follows through.
The process is quick and designed to work across different languages and literacy levels. It does not rely on personal financial data, which also helps address privacy concerns that are becoming more important in many markets.
For lenders, this creates an additional layer of insight at the point of application. It does not replace existing data where it exists. It strengthens decisioning where data is limited.
In Colombia, Begini worked with Duppla, a proptech platform focused on expanding access to home ownership through rent-to-own.
Duppla needed a way to assess applicants who would not pass through traditional credit checks, while still maintaining control over portfolio risk.
The behavioural assessment was integrated directly into the application journey. The deployment was fast, completed within a day, and designed to fit into a mobile-first experience.
The outcome was strong. Application completion reached 93 percent. More applicants could be assessed with confidence, including those without formal credit histories. The platform gained an additional signal to support approval decisions at the point of entry.
This kind of result is consistent with broader trends. Research from the World Bank highlights the role of alternative data in expanding access to financial services without weakening risk controls, particularly in emerging markets.
Around 1 in 4 adults in Latin America remain unbanked, according to the World Bank. Housing demand across the region continues to outpace supply, particularly for lower and middle-income households.
Rent-to-own is only one example. Similar challenges appear across other housing models that are now being tested and scaled across LATAM.
Some platforms are exploring progressive ownership, where customers increase their share in a property over time. Others are designing mortgage products that adjust to fluctuating income. Developers are also starting to offer embedded finance within property platforms.
In each case, the same gap appears early in the journey. There is limited data to support a confident decision.
Behavioural insight helps close that gap. It gives lenders a clearer view of how applicants are likely to behave, even when traditional data is missing.
This is one of the most common questions, and it is a fair one.
In practice, better data leads to better segmentation. Some applicants who look risky based on limited information turn out to be reliable when behavioural signals are considered. Others may show early signs of higher risk that would not appear in a thin file.
This allows lenders to refine approvals rather than simply expand them. The goal is not to approve everyone. It is to approve the right customers, and to do so with more confidence.
Digital adoption across the region is moving quickly. Prop-tech platforms are scaling. Regulators are paying closer attention to fairness, transparency and inclusion.
At the same time, the demand for housing continues to grow. Without new approaches to credit assessment, a large portion of the population will remain outside the system.
Behavioural data is not a standalone solution. It works best as part of a broader risk strategy. But it plays a clear role in enabling models that would otherwise be difficult to scale.
They combine new sources of data with simple, accessible user journeys. Behavioural assessments provide insight into decision-making patterns. This helps lenders evaluate applicants who do not have a formal financial footprint.
Housing finance is changing in LATAM. New models are starting to reflect how people actually live and earn. For these models to work at scale, risk assessment needs to evolve as well. Behavioural data offers a way to do that. It brings more people into consideration while helping lenders stay in control of performance.
That balance is what will define the next phase of housing access in the region.
Behavioural data looks at how people make decisions rather than relying only on traditional financial history. In lending, this can include signals linked to consistency, planning, and risk behaviour gathered through short digital assessments.
Rent-to-own platforms often work with customers who have limited credit history. Behavioural data provides an additional layer of insight that helps lenders assess applicants more confidently at the point of application.
Behavioural data can be used as a standalone credit assessment solution for some lending types, however it is Begini’s experience that for rent-to-own models, behavioural data works best alongside existing risk tools. It strengthens decision-making where traditional data is limited, incomplete, or unavailable.
Large parts of the LATAM population remain underserved by traditional financial systems. Many people have informal income or thin credit files, which makes conventional underwriting difficult. Alternative data approaches help lenders reach these segments more effectively.
Integration can be very fast depending on the platform setup. In Begini’s work with Duppla, deployment was completed within a single day.
Behavioural insight can support rent-to-own, progressive ownership, embedded property finance, flexible mortgage products, and other digital lending models designed for underserved borrowers.
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