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ScAIfe

Banking & Fintech: The AI layer that turns credit data into smarter decisions — from origination to recovery.

We’re building a predictive model integrated into the credit cycle, from the commercial offer through delinquency management. Built for financial institutions in LatAm, Spain and Portugal.

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  • Lower delinquency
  • More profitable approvals
  • Higher recovery
  • More effective contact
  • Better decisions
  1. DataIn-house data and alternative variables
  2. AI layer5 models, from origination to recovery
  3. DecisionsContinuous, explainable scores in real time

Sound familiar?

Data that doesn’t turn into decisions.

  • You make credit decisions with static, outdated scoring
  • You can’t predict how your portfolio will behave
  • You manage delinquency without knowing who to prioritize
  • You don’t know when or through which channel to contact each customer

That’s why we’re building models that turn data into decisions.

End to end

AI at every stage of the credit cycle

We support banks and fintechs with AI solutions applied to every process in the credit cycle.

Stage 02

Origination

Probability of Default and optimal credit amount models

Models

Where AI creates value across the credit cycle

Five models for the full credit cycle.

  1. 01
    LOWER DELINQUENCY

    Probability of Default (PD)

    Propensity to default

    A machine learning model that replaces static bureau scoring with a continuous probability of default (0–100%), calculated in real time using in-house data and alternative variables.

    What it delivers

    • Continuous score instead of a binary decision
    • Early detection of deterioration
    • SHAP explainability per case
    • API response in under 200ms
  2. 02
    MORE PROFITABLE APPROVALS

    Optimal credit amount

    By customer profile

    Calculates the recommended maximum amount and the optimal term based on each applicant’s real repayment capacity. The offer is generated dynamically, before a credit officer steps in.

    What it delivers

    • Offer 100% tailored to each profile
    • Less over-indebtedness
    • Higher approval rates among underserved customers
    • Integration without replacing the existing system
  3. 03
    HIGHER RECOVERY

    Propensity to pay

    Collections optimization

    Predicts which delinquent customers are most likely to pay in the coming days, so you can prioritize collections and focus resources where the impact is greatest.

    What it delivers

    • Smart portfolio prioritization
    • Higher recovery with the same team
    • Segmentation by probability and amount
  4. 04
    MORE EFFECTIVE CONTACT

    Best time and channel to contact

    Outreach optimization

    Determines, for each customer, the optimal channel and the time of day with the highest probability of effective contact.

    What it delivers

    • Higher effective contact rate
    • Less intrusive customer experience
    • Fewer failed attempts
  5. 05
    BETTER DECISIONS

    Smart portfolio segmentation

    Strategic portfolio view

    Clustering and automatic classification by behavior, risk and potential, to design differentiated strategies by segment and anticipate how the portfolio will behave.

    What it delivers

    • Actionable segments, not just descriptive ones
    • Opportunity detection among underserved customers
    • Foundation for retention and cross-sell strategies

First model

Real-time scores, explainable case by case

Facts about our Probability of Default (PD) model and the platform where we are testing it.

financial institutions use the platform where we are testing the PD model
500+
Credit and collections management platform · LatAm and Spain
API response time
<200ms
PD model · in testing
continuous probability of default, not a binary decision
0–100%
PD model · in testing
explanation included with every score
SHAP
PD model · in testing

Active development

Our first model — Probability of Default (PD) — is in testing against real historical data, on a credit and collections management platform used by more than 500 financial institutions in LatAm and Spain. We’re looking to activate this and the next credit-cycle use cases through distribution alliances with similar platforms.

Every score we generate comes with a SHAP-based explanation, built for environments where automated credit decisions must be explainable.

Why ScAIfe?

Companies know they need technology, but they don't know where to start or how to turn it into concrete results.

ScAIfe was born to close that gap, with two complementary profiles: business and technical execution.

  • 15+ years in banking & fintech
  • Fintech Américas Award
  • AI applied to real processes
Meet the team

  • Sergio Orcellet

    Founder & CEO

    15+ years at Banco Galicia leading digital products and operations in banking and fintech. Financial Innovation Award, Fintech Américas 2026.

  • Kevin Soulier

    Co-Founder & CTO

    10+ years in enterprise software engineering. Currently builds production AI at EY. Master's in Artificial Intelligence in progress (Universidad de San Andrés).

Frequently asked questions

AI for banking and fintech: what you need to know

Which AI models are you building for banks and fintechs?

Five models for the credit cycle: probability of default (PD), optimal credit amount, propensity to pay, best time and channel to contact, and smart portfolio segmentation.

How is the PD model different from bureau scoring?

It replaces static bureau scoring with a continuous probability of default (0–100%), calculated in real time using in-house data and alternative variables. Instead of a binary decision, you get a continuous score.

Can every score be explained?

Yes. Every score we generate comes with a SHAP-based explanation, built for environments where automated credit decisions must be explainable.

What is the current status of development?

Our first model, Probability of Default (PD), is in testing against real historical data, on a credit and collections management platform used by more than 500 financial institutions in LatAm and Spain. We’re looking to activate it and the next use cases through distribution alliances with similar platforms.

Do I have to implement every model at once?

No. You can start with a single part of the cycle: we begin with the model that has the most impact on your operation today.

Contact

Where in the cycle do you want to start?

You don’t need to implement everything at once. We start with the model that has the most impact on your operation today.

How do we start?

  1. 01

    Discovery call

    We understand your business and identify where technology can generate the greatest impact.

  2. 02

    Business and technology analysis

    We evaluate the problem from both a business and a technology perspective to define the best approach.

  3. 03

    Proposal

    We design a solution aligned with concrete results.

  4. 04

    Implementation

    We execute and deliver a solution ready to generate value.

  • We validate before building
  • Experience in real business solutions
  • End-to-end support

We reply within 24h

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