Market & Trends · By Lenext Team · Published on 2026-08-11 · 8 min read
Credit and collections in Brazil: the state of the industry and the 7 challenges holding operations back
An outlook on credit and collections in Brazil — record default levels, the cost of data, unstandardised decisions, analyst scarcity, regulation and fraud. With a three-level maturity map.
Brazil has reached 9 million companies in default, with roughly R$ 230 billion in overdue debt formally listed (Serasa Experian, 2026). Micro and small businesses account for most of that volume.
The figure is big enough to make headlines and vague enough to generate no action. It describes the outcome, not the cause.
Anyone who runs credit and collections day to day knows the problem is rarely the customer who went under. It is the sum of seven structural bottlenecks that make an operation decide slowly, overspend on data, and notice far too late that something changed.
First, the picture
Four forces are acting at once, and they feed each other.
| Force | What is happening |
|---|---|
| High, persistent default | Brazil's banking federation projects default in the free-rate portfolio above 5% in 2026, with a high volume of debt renegotiations |
| Expensive credit | With the policy rate still in double digits, the cost of corporate lending keeps every real trapped in receivables too expensive to ignore |
| Selective credit | A poor history makes access dearer and narrower: those who most need working capital face the biggest barrier |
| Competition that requires terms | 77% of B2B transactions in Brazil are settled on terms (Qive, 2026). Not selling on terms is not a commercial option |
The result is a squeeze: the company has to sell on terms to compete, while the cost of money and the risk of not being paid sit at their highest levels of the decade. It is under that pressure that internal bottlenecks stop being an annoyance and become losses.
The 7 challenges
1. Deciding credit on incomplete information
Bureaus answer well for those who already have a history. The difficulty is in the tail: young companies, no audited statements, no consolidated track record, no banking relationship deep enough to read.
That information asymmetry pushes the operation towards two equally expensive extremes: rejecting good customers out of caution, or approving blind under commercial pressure. Neither appears in any report.
2. Paying dearly for the wrong data, in the wrong order
This is the quietest bottleneck and the easiest to fix.
In a workflow with roughly 15% approval, up to 85% of query spend is consumed by companies that a free registry rule would have eliminated — irregular status, insufficient time in business, sector outside the appetite. The expensive query runs before the cheap rule simply because that is how the workflow was born.
Reordering queries by increasing cost has already produced up to a 64% reduction in cost per approved customer in real operations. It is the rare case where the saving requires no new technology — only sequence.
The other side of the coin is worth remembering: poor data quality costs companies an average of US$ 12.9 million a year (Gartner). Sophisticated analysis on bad data is sophisticatedly wrong.
3. Decisions that do not repeat and cannot be explained
If two analysts, facing the same customer, reach different decisions, the operation does not have a policy — it has opinions.
The classic symptom: the policy exists in a Word file, describes intent, and the system executes something else. Nobody compares the two documents, and the gap only surfaces when an auditor, a committee or a due diligence process asks for the decision trail.
There is an operational aggravating factor: in many companies, changing a credit rule in the ERP means opening a ticket and joining the IT queue — around three weeks on average. A policy that takes three weeks to change cannot respond to a scenario that changes weekly.
4. Nobody owns the policy
This is the structural challenge of the industry, and the hardest to see from the inside, because every system involved works — each doing one thing well, and none doing the thing that is missing.
| What the operation already has | What it actually does | What remains unanswered |
|---|---|---|
| Credit bureau | Supplies the data | Does not decide, does not apply policy, does not monitor the portfolio |
| ERP credit module | Blocks the order against a stored limit | Does not analyse, does not calculate limits, does not manage exceptions |
| Collections agency | Acts after the case has become a loss | Does not prevent, gives no visibility of custody |
| Spreadsheet + senior analyst | Works — until volume grows | Does not scale, cannot be audited, leaves with the analyst |
| Credit policy in a Word file | Describes the intent | Is not executed, is not versioned, does not match the system |
The bureau sells the data, the ERP blocks the order, the agency acts when it is already late. Nobody owns the policy — and it is the policy that decides.
5. Collections runs into a headcount ceiling
The recovery window is short and measurable: more than 82% of B2B debts up to 10 days overdue are recovered; after day 20, around 50%.
No human team handles 100% of the portfolio inside that window. The team calls the large invoices; the rest waits. And the rest is where recovery quietly rots.
Add the structural scarcity of qualified credit and collections analysts, and the bottleneck is clear: an operation's contact capacity is limited by people, and that is what caps scale. Automating transactional volume is what frees the existing team for the cases that genuinely require judgement.
6. Scaling without losing traceability
The counterweight to the previous challenge. Many operations stall collections automation not for lack of tooling, but out of legal exposure: automation advances, the audit trail does not follow, and control functions start acting as a brake.
The requirement is twofold and simultaneous. Consumer protection law (articles 42 and 42-A of Brazil's code, applied as a standard of conduct): no exposure to ridicule, embarrassment or threat; contact during business hours; limited attempts; no disclosure of the debt to third parties. Data protection (LGPD): purpose, necessity and transparency in processing, with a documented legal basis — including for data cleansing and enrichment.
The synthesis the industry is converging on: a good dunning journey collects and preserves evidence.
7. Fraud that became cheap to produce
Brazil recorded 34.5 billion digital fraud attempts between March 2025 and February 2026, with estimated losses of R$ 21.2 billion (Poder360, 2026). Document fraud is already a serious problem for 60% of companies that depend on checking third-party documents.
The change in nature matters more than the volume: synthetic identities assembled from real and fictitious data, and documents manipulated to a quality that visual checking will not catch. Deepfake use grew 126% in Brazil between 2024 and 2025.
For anyone granting B2B credit, that means identity and registry validation has stopped being a bureaucratic step and become a decision layer — on the same level as the score.
What is moving in your favour
Not everything in the scenario pushes against you. Three movements opened new capacity in recent cycles.
Open Finance as a decision source. More than R$ 31 billion in credit has already been originated using Open Finance data in Brazil. The main gain is not for those who already have a history — it is for thin-file profiles, which can now be read through actual transaction flow rather than absence of information. For the corporate segment the use is still maturing, and it is worth treating it as data infrastructure rather than a guaranteed drop in default.
Pix Automático. Since the January 2026 regulatory cut-off, recurring collection over Pix has removed an entire class of involuntary default — expired cards, declined authorisations, banking arrangements out of reach for smaller companies.
Decision automation as a mature category. The global decision intelligence market went from US$ 13.3 billion in 2024 towards a projected US$ 50.1 billion by 2030. Translated to the operation: decision engines, automated reading of financial statements and continuous monitoring have stopped being bank-scale projects and become off-the-shelf items for companies that sell on terms.
Where your operation stands
A simple three-level map to locate the operation before choosing the next move.
Level 1 — Reactive. The policy lives in the heads of two or three people. Analysis is a spreadsheet plus a bureau query. Collections start after the due date and reach the large invoices. Nobody measures cost per approved customer. Next step: write the policy and measure what already happens.
Level 2 — Formalised. There is an approved document, an approval matrix and a defined dunning journey. But execution still depends on people: rules are not parameterised, exceptions are not counted, and the portfolio is only reviewed when something breaks. Next step: turn written rules into executable rules and switch on continuous monitoring.
Level 3 — Executable. The policy runs in the engine, with versioning and an audit trail. The workflow queries in cost order. The dunning journey covers 100% of the portfolio from day one. The indicators — including cost per approved customer and exception rate — are on the committee's monthly agenda. Next step: calibrate by vintage and expand by product and business unit.
Most Brazilian B2B operations sit at level 1 or on the border between 1 and 2. And the distance between levels is not a budget question — it is a decision about who owns the policy.
Credit and collections is a more technical, more regulated and more data-driven industry than it was three years ago. Nine million companies in default is the country's picture; cost per approved customer, the share of the portfolio contacted by day ten, and the number of unlogged exceptions are your company's picture.
Which of the three levels is your operation on today — and which single bottleneck, once solved, would move the other six?