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.
Behavioural credit scoring uses behavioural data to help predict repayment outcomes. Traditional credit scoring relies heavily on historical financial records such as loans, repayment history and credit bureau data. Behavioural credit scoring adds another layer of insight by analysing behavioural patterns associated with future financial behaviour. This can include indicators linked to consistency, decision-making, self-control, planning behaviour and follow-through.
For lenders, the value is clear. Behavioural credit scoring can help assess applicants who have little or no traditional credit history, making it particularly useful in emerging markets and financially underserved segments. In housing finance, it provides an additional perspective when evaluating prospective homeowners.
For many households across LATAM, the path to home ownership remains difficult.
Banks often require significant deposits and strong credit profiles. At the same time, millions of people earn income through informal or semi-formal channels, making it harder to satisfy traditional underwriting criteria.
The result is a large group of potential homeowners who fall between the cracks. They are not necessarily high risk. They are simply difficult to assess using conventional credit models.
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.
This challenge has helped drive the growth of alternative housing finance models, including rent-to-own programmes and progressive ownership schemes.
These approaches recognise that home ownership is often a journey rather than a single transaction.
Rent-to-own housing is becoming increasingly attractive because it provides a practical pathway for people who may not yet qualify for a traditional mortgage.
One company addressing this challenge is Duppla in Colombia.
Duppla’s model is built around partnership. In fact, the company’s name reflects the idea of partnering with customers throughout their journey towards home ownership.
Many applicants arrive with approximately 15% of a property’s value saved as a deposit. Traditional lenders in the market often require closer to 30%.
Rather than competing with banks, Duppla helps bridge that gap.
Customers can begin their home ownership journey while continuing to build their financial profile. Over time, successful customers may transition into traditional mortgage products offered by banking partners.
This creates value across the ecosystem.
Borrowers gain access to housing opportunities that may otherwise be unavailable. Banks gain access to a pipeline of customers who have demonstrated commitment and financial discipline. Housing providers benefit from a larger addressable market.
But one important challenge remains.
How do you identify which applicants are most likely to succeed?
When Duppla began working with Begini, the conversation was not simply about credit risk. It was about understanding commitment.
Duppla refers to Begini’s behavioural assessment as an “intent to pay” score.
Traditional underwriting focuses heavily on a customer’s ability to pay. Behavioural credit scoring adds another perspective by helping lenders understand a customer’s likely commitment to repayment.
Through a short mobile-based assessment, lenders gain insight into behavioural characteristics associated with successful repayment outcomes.
These include indicators linked to consistency, self-control, decision-making patterns, planning behaviour and follow-through.
Together, these signals help create a more complete picture of the applicant.
For Duppla, behavioural credit scoring provides an additional decisioning layer that helps identify applicants who may be overlooked by traditional credit models alone.
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.
“The psychometric score is critical in our customer acceptance policy. Begini enables us to analyse risk in ways traditional systems cannot, which helps us extend access while maintaining portfolio quality.”
– Nicolás Ayala, Duppla.
Housing in Colombia, where new models are helping those previously invisible to lenders.
Behavioural credit scoring uses behavioural data to help predict repayment outcomes. It analyses behavioural patterns associated with future financial behaviour and provides lenders with an additional source of insight alongside traditional credit information.
Traditional credit scoring relies primarily on historical financial information such as loans, repayment records and credit bureau data.
Behavioural credit scoring uses behavioural signals associated with future repayment performance. Many lenders use both approaches together to improve credit decisioning, particularly when assessing applicants with limited financial history.
An intent-to-pay score uses behavioural data to help lenders understand characteristics linked to repayment commitment and financial discipline. It provides an additional perspective alongside traditional credit information.
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.
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.
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.
Begini brings real-time, behavioural credit assessment to lenders across emerging markets — so you can grow your book into the borrowers traditional models never saw. Let’s talk about your portfolio.
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