83 million Brazilians are credit-flagged. And millions of good payers are invisible. Does your rule tell them apart?
Kavuka Credit Score combines the bureau with the layers it lacks — alternative data, deep ownership and litigation reading, company-group analysis and the separation of credit from fraud — returning a score calibrated to your product, with limit, price and pipeline by tier.
- Seconds
- per decision
- Beyond the bureau
- alternative data
- Company + group
- economic reading
- Anti-synthetic
- native layer
Credit engine in production scoring origination for fintechs, retail and B2B credit — thousands of analyses per day, with a per-product calibrated score and a full decision trail.
Every month credit loss and fraud arrive on the same bill — and the wrong rule keeps running.
The good customer your rule rejected
A generic score rejects those who cleared their debts, those who never had credit and the thin-file payer off the radar — the young, the informal and micro-entrepreneurs left out of your market.
The "default" that was fraud
Part of the loss is not credit: it is synthetic identity that built a score on purpose. The rule treats as default what was never a customer — and your margin pays for it.
The company without the group behind it
A company assessed by its registration alone — no ownership, economic group, partner litigation or real capacity — and an automated decision with no traceability or explanation, against data-protection law.
Cost With 83.3 million credit-flagged people in April 2026 (Serasa Default Map, a record after 16 months of increase), the binary rule excludes a third of the consumer market. And among companies, Brazilian businesses closed 2025 with R$ 213 billion in debt and record default — selling on credit without deep reading of the company and its group is a giveaway.
From bureau to an explainable decision, in a single call.
- 01
Query
Bureau (including the positive registry via partners) and the Kavuka layers — ownership, litigation, economic-activity signals — in a single call.
- 02
Weigh
A score calibrated to your product and your real loss — not the market average. Companies assessed with their economic group, partners and matching capacity.
- 03
Separate
The anti-synthetic signal runs alongside: identity fraud is blocked or flagged before it becomes "default" — credit measures only credit.
- 04
Decide
Limit, rate, term and pipeline by risk tier (risk-based pricing), with explanatory factors on every decision and a full trail.
The engine behind every credit decision
A single query cross-references the bureau with deep alternative data and returns a calibrated score, ready to price and automate credit approval.
Calibrated score
By product and by real loss
Bureau data
History and positive registry via partners
Alternative data
Activity, stability and relationship signals
Company economic group
Ownership, group and partner litigation
Anti-synthetic layer
Identity backing and relationship graph
Litigation risk
Civil, tax and partner lawsuits
Financial restrictions
Credit flags, protests and tax status
Decision engine
Configurable policy, tiers and pricing
Who decides credit with Kavuka
Credit fintechs & BNPL
Digital origination with a calibrated score and native anti-synthetic defense, separating credit from fraud at the decision point.
Retail & In-store credit
Checkout approval in seconds, with risk gradation instead of the binary stamp — more sales without more loss.
B2B Credit & Supply Finance
Deep company reading — economic group, partners and real capacity (bridge with KYB) — to sell on credit without giving away margin.
Leasing, Subscriptions & Collections
A default score for recurring contracts and a recoverability score — the right rule for each portfolio tier.
Automated credit decisions — and explainable ones
Kavuka Credit Score was designed for the automated decision that data-protection law requires: each analysis carries the factors that explain it, the right to review is honored and the credit policy is fully traceable. Explainability is not a compliance attachment — it is part of the product.
- Explanatory factors on every decision: the customer and the regulator understand why credit was granted, denied or priced.
- Right to review automated decisions honored, per data-protection law.
- Lawful use of data: public or legally permitted sources, with an adequate legal basis for credit granting and management.
- Full per-decision trail: score, factors, sources and date — for audit and to defend the credit policy.
- Encryption in transit and at rest; DPA available for enterprise clients.
We swapped the binary check for a graded score and approved a whole band of thin files we used to reject. Loss did not rise — revenue did.
We found that part of our "default" was never a customer: it was synthetic identity. Separating it cleaned both the rule and the loss budget.
In B2B credit, assessing the company with its economic group behind it changed the big decisions. We stopped selling on credit in the dark.
Bring a sample of your portfolio: we show how much the complete reading would have changed.
We return the simulation with your volume and your product — more approvals where there is a good payer, less loss where there is real risk and fraud out of the credit bill.
- For businesses only. No purchase commitment.
- Data used solely for commercial contact.
- Enterprise leads answered within 1 business day.
What a credit score is and how to read the whole risk
A credit score is the quantification of the probability of default of a person or company — the central instrument of credit granting, limit, price and collection. It answers, in a single number, the question that defines any operation that sells on credit: how likely is this customer not to pay? The better the risk reading, the sharper every decision to approve, decline or price. Kavuka Credit Score is the credit product built on the Risk Scoring engine: it combines traditional data — history, restrictions and the positive registry via partner bureaus — with the layers generic scores lack.
These layers are three: alternative data (deep ownership, litigation, economic-activity signals), thin-file reading — the good payer invisible to traditional credit, such as the young, the informal and micro-entrepreneurs — and the decisive frontier of the current era: separating credit risk from fraud risk. Part of Brazilian "default" is not poorly granted credit: it is undiagnosed synthetic-identity fraud, a "debtor" who never existed and built a score on purpose. Treating both losses with the same rule distorts the risk budget and the granting decision.
The Brazilian context makes this urgent. Credit operates in the country with default at a historic high: the Serasa Default Map recorded 83.3 million credit-flagged people in April 2026, a record after 16 consecutive months of increase — over ten years, the contingent grew 38%. The positive registry, in force since 2019, expanded the raw material and made payment history the heaviest factor in market scores (around 29% in the Serasa Score). Yet the generic rule keeps leaving two kinds of money on the table: the good customer outside the average curve and the loss that is not credit, but fraud. Among companies the picture is just as tense — Brazilian businesses closed 2025 with R$ 213 billion in debt.
The benchmark lesson confirms the path. FICO, the global standard used by the vast majority of large US lenders, is moving its frontier toward financial inclusion: UltraFICO and models with permissioned cash-flow data read thin files, specific variants capture BNPL behavior and transparency is treated as a product differentiator. The three lessons: thin file is the category growth frontier, and alternative data is the answer; per-product variants beat the single score; explainability is product, not compliance. The Kavuka space is not to compete with the bureau generic score, but to sell the missing layer on top of it — per-business calibration, deep alternative data, company-group reading and the credit-versus-fraud separation no bureau delivers. The result is direct: approve more where there is a good payer, lose less where there is real risk, price correctly for each tier — credit with the complete reading.
Does Kavuka Credit Score replace the bureau?
No — it adds to it. The query combines bureau data (including the positive registry, via partners) with the Kavuka layers: ownership, litigation, economic activity and group. The result is a score calibrated to your product, not the market average.
How does thin-file reading work?
Through alternative data: activity, stability and relationship signals that exist even without a credit history. The invisible good payer gets a reading — and your operation gains the market the binary rule excludes.
What is the credit-versus-fraud separation?
Part of default is synthetic-identity fraud: the "debtor" never existed. The Kavuka anti-synthetic layer (identity backing, relationship graph) runs alongside the score — fraud is blocked or flagged, and your credit loss finally measures only credit.
Does it work for companies?
Yes — with the reading a company requires: registration and tax status, ownership and economic group, partner litigation and matching capacity. The company is never assessed in isolation.
Does the automated decision comply with data-protection law?
Yes: explanatory factors on every decision, the right to review honored and a full trail. Explainability is part of the product, not a report at the end.
What is risk-based pricing and why does it matter?
It is pricing limit, rate and term according to the real risk of each tier, instead of a single price for different risks. With a calibrated score, the good payer earns better terms and real risk is priced correctly — more approvals and less loss in the same portfolio.
How much of Brazilian default is actually fraud?
That is exactly the question the generic rule cannot answer. Part of the loss booked as default is undiagnosed synthetic-identity fraud. By running the anti-synthetic signal alongside the score, Kavuka Credit Score measures and separates the two losses — and the credit budget reflects only credit.
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