Alternative data and open finance: deciding where the bureau falls short

What counts as alternative data (and what signal it adds)

Alternative means anything that does not come from traditional credit history. These are the families with the most traction across Latin America:

• Open finance and bank transaction data: income, seasonality and balance behavior — a potentially very valuable source for estimating repayment capacity, whose predictive power must be validated for each population.
• Digital payslips and payroll records: direct income verification for formally employed applicants.
• Telco and utility payments: payment regularity and tenure, a signal of reliability where no financial history exists.
• Your own business data: in retail and wallets, how a customer behaves with your house — purchases, top-ups, returns — often predicts better than any external source.
• Device and process signals: more useful for fraud than for credit — it is best not to blend the two conceptually.

Integrating sources: each one is a node, not a project

The historical barrier to alternative data was never the signal — it was the engineering: every new provider meant months of integration work. In the decision engine, a new source is a REST node with its parser. You configure it against the provider's API, map the variables it returns, and those variables become available to your rules like any other.

The same disciplines that apply to credit bureau orchestration apply here: waterfall logic so expensive calls happen only when they are still needed, timeouts and fallback paths because new providers fail more often, and raw responses archived so you can audit exactly what the source reported.

Validate before you trust: new data has to earn its place

No source gets to decide on faith. A healthy adoption path has three stages:

• Shadow calls: the policy queries the source and records its variables without using them in the decision — you accumulate real data at no risk.
• Backtesting: with the accumulated history (or a batch run over past applications), you measure whether the new variable separates good payers from bad ones.
• Champion/challenger testing: the policy with the new data competes against the live one on a share of traffic, with actual delinquency as the judge.
• Only then does the source gain weight in the general policy — versioned, measurable and reversible like any other change.

Governance of alternative data

More sources means more governance surface: consent from the data subject wherever regulation requires it (open finance is consent by design), cost per query controlled through the waterfall and the re-query window, and per-source monitoring of availability, latency and signal drift.

Decision traceability closes the loop: if a decision used telco or transaction data, the transaction records that fact along with the provider's original response. When the customer or the regulator asks, the evidence is already there.

The practices described here should be adapted to the regulation, credit policies, and consumer and data-protection obligations applicable in each country.