Territory as a risk variable.
The same declared income means something different in a highly central urban neighborhood than in a peripheral one. The environment explains what the applicant's history can't see.
Environmental variables by address: income, habitat, housing, mobility
Integration via API or batch file over your portfolio
Pilot on a sample to measure predictive uplift
Trusted by
The problem
How we solve it
El problema y cómo lo solucionamos
Your model works with the applicant's history and declared income, but knows nothing about the environment where that person lives or where the asset is located.
We enrich every address in your portfolio with environmental variables: area income, habitat type, housing stock, mobility, and commercial offering.
The same profile and the same income behave differently depending on the neighborhood, and the model can't tell them apart.
We integrate wherever it suits you: real-time REST API per application, or a batch file over the full portfolio.
When you want to add environmental variables, there's no homogeneous address-level data covering the whole portfolio.
We measure the improvement in predictive power on your own model before you decide to integrate: a pilot on a sample of your portfolio.
What you receive
Environmental variables
By address, via API or file
How much does your model improve?
Measurement of the predictive power uplift on your model
Final result
Your enriched portfolio, ready to retrain the model.
Which variables we add to your model
Area income
Average per-capita and per-household income of each address's surroundings.
Habitat type
Central urban, peri-urban, or rural, with density and centrality.
Housing stock
Typology, construction quality, and age of housing.
Commercial offering
Density and type of surrounding retail and services.
Accessibility
Access to transport, healthcare, education, and financial services.
Mobility and presence
Pedestrian and vehicle flows, tourists, and transient population.
How it works
01
Tell us your case
Two minutes with the form. No prior calls or commitment.
02
First assessment within
48 h
We tell you whether we can help, how, and with what approach.
03
30-minute call
If it's a fit, we dive deep into your case and scope the proposal.
We work with major accounts in banking, insurance, energy, and consumer goods.
FAQ
Which environmental variables improve a scoring model?
The most relevant are average area income, habitat type (urban/peri-urban/rural), commercial density, housing typology, ratio of homeowners to renters, and accessibility to financial services. These variables correlate with payment behavior and reduce model uncertainty.
How does it integrate into my stack?
Via API or through delivery of an enriched file.
Is it GDPR-compliant?
The variables we add are aggregates of the surroundings at 100m grid level, not personal data about third parties: they don't bring any individual or identifiable information into the model. We don't need you to hand over any personal data or any data subject to GDPR.
Does your scoring model take territory into account?
Ours does. And we'll integrate it into yours.