The generic score was built for the average. Your business is not the average.
Kavuka Risk Scoring builds your business’s own score: you define the decision, we build the model with thousands of variables from Brazil’s deepest data, prove it against your history (backtesting) and run it in real time — with explainability on every decision.
- Thousands
- of features in the store
- Milliseconds
- per decision in production
- Backtesting
- proven against your history
- Explainable
- the why behind each score
Engine in production quantifying risk for credit, insurance, marketplaces and logistics — millions of decisions with explainability and continuous model monitoring.
Every day your operation decides with a ruler that was not built for your business.
Someone else’s ruler
The generic bureau score was calibrated for the market average — not for your product, your audience or your loss. Your data team decides in the dark, without access to rich external variables.
The false no (and the false yes)
The customer who would be good for your product is rejected by the wrong ruler; the loss the generic score never saw gets approved. Both errors are invisible in the report — and both cost.
The indefensible black box
A model with no explanation to the data subject leaves Compliance exposed: data-protection law grants the right to review automated decisions, and nonexistent model governance does not hold up in an audit.
Cost The cost of inaction is double and invisible: the false "no" — the good customer rejected by the wrong ruler, whom the competitor approves — and the false "yes" — the loss the generic score was never trained to see. No one measures the customer who didn’t enter, nor the cause of the loss that did.
From your business problem to the score that decides, in four steps.
- 01
Define the decision
Together we frame the event to predict (the target) and the loss to avoid — credit, fraud, claim, seller, recoverability.
- 02
Build
The Brazilian feature store cross-references registry, ownership, judicial, financial and behavioral data; modeling ranges from weighted rules to supervised ML.
- 03
Prove
Backtesting against your own history: would the score have gotten the past right? How much loss it would have avoided, how much approval it would have kept — before production.
- 04
Operate
Real-time API with configurable bands and cutoff policies, an explanation on every decision and drift monitoring that warns before the error.
The engine behind every decision
A quantitative infrastructure that goes from raw variable to actionable number — calibrated for your decision, not the market average.
Brazilian feature store
Thousands of variables from our own engines
Modeling
From weighted rules to supervised ML
Calibration and backtesting
Proof against history before production
Real-time decisioning
Millisecond API, configurable bands and cutoffs
Explainability
Every score carries its reasons
Model monitoring
Drift, performance and continuous recalibration
Ownership and judicial depth
Variables the bureaus don’t have
Model governance
Version, rationale and trail per decision
Where the custom score decides
Credit & BNPL
Custom origination and behavioral scores, when the off-the-shelf Credit Score isn’t enough for your product.
Pricing & claims anti-fraud
Acceptance and pricing by the insured’s real risk; a barrier to fraud at claim opening.
Marketplaces & platforms
Seller, buyer and transaction scores — trust calibrated to your internal economy.
Logistics & collections
The Driver Score / Cargo Intelligence family is born from this engine; in collections, the recoverability score prioritizes effort by expected return.
Automated decisioning that holds up before regulators and data-protection law
Explainability is not a report at the end — it is how the engine operates. Each score carries the factors that produced it, meeting the right to explanation and review of automated decisions, with a full trail.
- Per-decision explainability: each score carries its predictive factors, in auditable language.
- Right to review of automated decisions met by design, not by exception.
- Model governance: versioning, rationale, metrics (KS/AUC) and owner on record.
- Public or legally permitted sources; encryption in transit and at rest.
- Data Processing Agreement and per-decision audit trail for enterprise clients.
Backtesting showed, before we signed, how much of our loss the score would have avoided while keeping approval. The decision to hire came from a number, not a promise.
We stopped approving and rejecting in binary. Today the score sets price, limit and friction by band — we approve more where we win and block where we were bleeding.
The last audit asked for the reason behind an automated decision. We delivered the factors, the model version and the trail. Zero findings.
Bring your history. We’ll show how much a custom score would have saved.
With a sample of your loss history, we return the backtesting: a simulation of what a custom score would have changed — before any decision to buy.
- For businesses only. No purchase commitment.
- Data used solely for commercial contact.
- Enterprise leads answered within 1 business day.
What Risk Scoring is and how to build a custom score
Risk Scoring is the engine that turns data into an actionable number: the score that quantifies the risk of a person, company, transaction or operation, in real time, calibrated for the client’s specific decision. It is the quantitative infrastructure of the Kavuka portfolio — Credit Score, Reputation Score, Driver Score and the fraud scores are all products built on this same engine. Risk Scoring as a solution is its customizable version: the client defines the decision, Kavuka builds the score, with variables from Brazil’s deepest data, explainable models and proof against history.
The problem it solves is deciding with someone else’s ruler. The generic bureau score was calibrated for the market average, with credit data — not for your product, your audience or your loss. That produces two invisible errors: the false "no", the good customer rejected by the wrong ruler (and approved by the competitor), and the false "yes", the loss the generic score was never trained to see. Neither shows up in the report, because no one measures the customer who never entered nor isolates the cause of the loss that did. A custom score, trained on your history and for your loss event, is what makes both costs visible and manageable.
Building a custom score is a five-component pipeline. The feature store gathers thousands of predictive variables (features) derived from our own engines — registry, ownership, judicial, financial, behavioral and geographic — many of which the bureaus do not have. Modeling ranges from weighted-rule models to supervised machine learning, always with explainability: each score carries its reasons. Calibration and backtesting test the model against the client’s own history — how much past loss it would have avoided, how much approval it would have kept — delivering the number before the decision to buy. Real-time decisioning responds in milliseconds, with configurable bands and cutoff policies. And model monitoring tracks drift and performance: a score that starts to age warns, and recalibration is proposed before the error reaches the portfolio.
Risk Scoring is also a compliance answer, not just a performance one. Data-protection law grants the data subject the right to explanation and review of automated decisions; a black-box model is indefensible before the regulator and the customer alike. That is why explainability sits at the core of the engine: every decision carries the factors that produced it, the model version and the full trail. Fittingly, the category benchmark — the FICO Platform, named a leader in the Forrester Wave for AI decisioning platforms — made exactly the journey from score provider to decisioning platform, with responsible AI and explainability as the differentiator that sells on both sides: regulator and customer. Kavuka’s space is not to compete with the generic bureau score, but to offer the score customized per decision — with deeper variables and calibration for the specific business. The bureau can even enter as one more variable in your score.
How is it different from a bureau score?
The bureau score is generic — calibrated for the market average, with credit data. Kavuka Risk Scoring is custom: trained on your history, for your loss event, with variables well beyond credit (ownership, judicial, behavioral). The two can coexist — the bureau can even enter as one more variable in your score.
Do I need a data team to use it?
No. Kavuka builds, tests and operates the model together with your business team. If you already have a data team, it gains the Brazilian feature store and the production infrastructure (real-time decisioning, explainability and drift monitoring).
How do I know the score works before using it?
Through backtesting: the model is evaluated against your own history — how much past loss it would have avoided and how much approval it would have kept. The number comes out before the decision to buy, not after.
Are automated decisions compliant with data-protection law?
Yes. Each score carries its explanatory factors, meeting the right to explanation and review of automated decisions, with a full per-decision trail (factors, model version and rationale).
Does the model maintain itself?
We monitor drift and performance continuously. When the world changes and the model starts to age, recalibration is proposed before the error appears in the portfolio — the aging score warns before it fails.
What kinds of decision is Risk Scoring for?
Any risk decision that deserves gradation instead of binary: credit and BNPL (origination and behavior), insurance (pricing and claims anti-fraud), marketplaces (seller, buyer and transaction scores), logistics (Driver Score / Cargo Intelligence) and collections (recoverability).
What is the difference between Risk Scoring and Kavuka Credit Score?
The Credit Score is a ready-made product built on the engine. Risk Scoring is the engine in its customizable form — for when the off-the-shelf score isn’t enough and you need a model calibrated for your specific decision and loss.
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