Across much of Latin America, lenders face a familiar dilemma: strong demand for credit, but limited information to assess it.
Millions of individuals and small businesses operate outside the formal financial system. They trade daily, generate income, and repay obligations, yet remain invisible or misrepresented in traditional credit bureaus. The result is a persistent credit gap that affects lenders and borrowers alike.
This is not simply a problem of inclusion. It is a problem of how risk is measured.
Credit bureaus were designed for formal economies with stable employment, documented income, and long financial histories. In those contexts, historical repayment data works reasonably well.
In informal or semi-formal economies, however, credit history often tells an incomplete story.
Many creditworthy applicants are classified as “thin-file” or “no-file” because they:
From a bureau-only perspective, these applicants look risky. In reality, many demonstrate strong financial discipline and intent to repay.
The issue is not that credit bureaus are wrong, it’s that they are insufficient on their own.
A lack of historical data is often treated as a proxy for risk. This assumption creates two structural problems for lenders:
1. False negatives: Creditworthy borrowers are rejected, reducing portfolio growth and lifetime value.
2. Blunt risk segmentation: Applicants are grouped into broad risk buckets that fail to reflect real behavioural differences.
In markets with high informality, risk is less about past access to credit and more about current behaviour.
How consistently does an applicant engage with a task?
How do they respond to situations involving decision-making and money management?
Do their patterns suggest care, attention, and intent. Or randomness and disengagement?
These signals exist long before a loan is issued. Traditional models simply do not capture them.
Across multiple emerging markets, lenders are already applying behavioural signals alongside traditional credit data.
In practice, this approach is most commonly used to better assess thin-file applicants, reduce false declines, and improve confidence in early-stage credit decisions — particularly in informal or mobile-first lending environments.
This is where behavioural analytics becomes relevant.
Behavioural analytics focuses on how people interact, not who they are. Rather than relying on demographics or identity attributes, it analyses behavioural patterns generated during a digital journey.
Examples of behavioural signals may include:
Individually, these signals may seem subtle. Collectively, they provide insight into reliability, intent, and engagement—factors that are highly relevant to credit risk.
Importantly, these signals are:
This makes behavioural analytics for credit risk them particularly valuable in emerging and informal markets.
A common misconception is that behavioural analytics is intended to replace credit bureaus or traditional risk models. In practice, the opposite is true.
The strongest outcomes are achieved when behavioural signals are used to complement existing data, not substitute it.
For lenders, this typically means:
This layered approach allows lenders to make more confident decisions without fundamentally changing their risk framework.
Financial inclusion is often framed as a trade-off: approve more borrowers and accept higher risk or protect the portfolio and exclude more people.
This is a false choice.
When lenders improve the quality of information used in decision-making, inclusion and risk management can move in the same direction. In fact, better information enables what can be thought of as a “risk swap” within the portfolio.
Rather than simply approving more applicants across the board, lenders are able to remove genuinely high-risk profiles earlier in the journey, while identifying and onboarding more low-risk borrowers who were previously excluded due to limited or incomplete data. The result is not a trade-off between growth and prudence, but a rebalancing of the portfolio itself.
In this context, financial inclusion is not about taking on additional risk. It is about replacing uncertainty with insight, swapping risk that was poorly understood for risk that is better measured.
Behavioural analytics for credit risk helps reduce uncertainty where traditional data is weak. This leads to:
In other words, better information leads to better outcomes, for both lenders and borrowers.
The “Risk Swap” Effect
Better information allows lenders to remove high-risk applicants earlier, while approving more low-risk borrowers who were previously excluded.
The result: inclusion and portfolio quality improve at the same time.
In markets like Mexico, Colombia, Peru, and Central America, lending journeys are increasingly digital, mobile, and high-volume.
Behavioural analytics fits naturally into these environments because it:
Rather than forcing applicants into rigid models, it adapts to how people actually behave.
The future of credit assessment in emerging markets will not be built on a single data source. It will be built on combining signals: historical, behavioural, and contextual, to create a more accurate picture of risk.
Credit history remains valuable. But on its own, it is no longer enough.
By incorporating behavioural analytics for credit risk, lenders gain access to a layer of insight that has long been missing, one that reflects how people act today, not just what they did in the past.
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