Programs like FGI-PEAC and BNDES Automático were designed around a specific risk equation: the Brazilian state, through a guarantee fund, absorbs up to 80% of the default risk on an SME credit operation. In exchange for that risk mitigation, the credit should reach the borrower at a rate consistent with the residual risk left for the bank. Five recent forensic audits, across different lending institutions, show that equation simply doesn't hold in practice.
It's a simple actuarial logic: lower risk for the lender should mean a lower price for the borrower. The set of cases reviewed here — five public-backed credit operations, ranging from roughly BRL 150,000 to BRL 800,000 — consistently shows that the risk discount granted by the state never reaches the company's bottom line.
In this analysis
A program designed to make credit cheaper
Programs like FGI-PEAC (the Guarantee Fund for Investments — Emergency Credit Access Program) and BNDES Automático's lines were built around a specific risk equation: the state, through a guarantee fund, absorbs up to 80% of the default risk on a credit operation granted to a small or medium-sized company. In exchange for that risk mitigation, the credit should reach the final borrower at an interest rate consistent with the residual risk left for the bank — significantly lower than what that same bank would charge on a free-market operation with no state guarantee at all.
It's a simple actuarial logic: lower risk for the lender should mean a lower price for the borrower. The set of cases reviewed in this article — five public-backed credit operations, across different lending institutions, ranging from roughly BRL 150,000 to BRL 800,000 — consistently shows that this equation does not hold in practice.
The pattern: risk mitigated, free-market pricing kept
In all five cases, the public guarantee fund covered most of the operation's credit risk — typically 80%. In none of them, however, did the interest rate charged proportionally reflect that risk reduction. Quite the opposite: in virtually every case, the effective rate was between 30% and over 130% above the program's own average reference rate for comparable operations — levels closer to an unsecured, free-market credit operation than to a subsidized line.
The multiple layers that compound the problem
In four of the five cases reviewed, assistance-spread capture came bundled with a second layer of collateral stacking: even with the public fund covering 80% of the risk, the contracts still demanded unlimited joint-and-several personal guarantees from the partners — sometimes from multiple guarantors — and occasionally a third layer of additional real or financial collateral (real estate, a fiduciary assignment of a financial investment, a capitalization bond). The bank's risk, in practice, ended up covered by three or four simultaneous instruments — while the price charged to the borrower stayed as if none of those protections existed.
Rate 30% to 130% above the program's reference. The contracted effective rate, even with 80% of the risk covered by the public fund, drifts far from the program's own reference parameter — in some cases, more than doubling the expected cost for an operation with that level of state guarantee.
Collateral stacking with no discount. In 4 of 5 cases, unlimited personal guarantees and additional real collateral stack on top of the public fund's 80% coverage, with no rate reduction corresponding to the risk reduction these extra layers represent for the lender.
A phantom balance in the amortization model. In one case, the bank's own calculation system never zeroes the outstanding balance at the end of the contracted term, even after regular, full payments — a structural error that compounds, rather than offsets, the above-parameter rate problem.
Tied sale of a capitalization bond. In another case, part of the disbursed amount was withheld as a mandatory investment tied to the credit — yet another way for the lender to extract extra value from an operation that should already be cheaper because of the public guarantee.
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Why this isn't an accusation against one bank
This pattern showed up across operations from distinct lending institutions within the sample of cases under review — it is not, therefore, the isolated practice of a single institution, but a behavior observed in this and other operations reviewed under forensic audit, which appears to reflect a commercial logic common to the segment when it operates public-backed or guaranteed credit lines. The pattern's spread across different lenders is, in fact, the most concerning finding: it suggests assistance-spread capture is not a one-off lapse in conduct, but a structural behavior of the SME banking-credit market when dealing with state guarantee programs.
What the forensic audit recalculates
The methodology applied in these cases consists of: establishing the fomento program's own average reference rate for operations of comparable risk and term; recalculating the operation's payment flow by substituting the contracted rate with one consistent with the residual risk actually assumed by the lender, given the guarantee fund's coverage; and isolating ancillary charges (financed fees, insurance, capitalization bonds) billed without a corresponding actual service.
What a business owner should demand
For the borrower, the lesson is clear: when contracting a publicly-guaranteed credit line (FGI-PEAC, BNDES, PRONAMPE), it isn't enough to compare the offered rate with "what the market charges" — it must be compared with the program's own reference rate, and the lender should be formally asked why a guarantee covering 80% of the risk doesn't translate into a proportional price discount.
- Demand the fomento program's reference rate for operations of comparable risk and term.
- Formally ask what share of the risk is covered by the guarantee fund.
- Add up every collateral layer required and assess whether it makes sense given the already-mitigated risk.
- Check whether any ancillary product (insurance, capitalization bond) was sold alongside the credit.
For the regulatory design of these programs, the pattern described here points to a supervision gap: without a mechanism to verify that the risk discount is being passed through to the final borrower, the public benefit risks being largely absorbed by the lender's margin — undermining the program's own assistance purpose.
This article is part of a series on technical pathologies in business credit operations, based on expert opinions prepared by the author. Individual cases are treated in aggregate and anonymized form, with no identification of the companies or individuals involved.
Frequently asked questions
What is assistance-spread capture?
It's the phenomenon where a bank reduces its own credit risk through a public guarantee (such as FGI-PEAC, which covers up to 80% of the risk), but does not pass the corresponding cost reduction on to the borrower — keeping the rate close to what a free-market operation, without any state guarantee, would cost.
Why should a publicly-guaranteed credit line carry a lower rate?
Because the actuarial logic of credit is: lower risk for the lender should mean a lower price for the borrower. When a guarantee fund absorbs 80% of the default risk, the bank's residual risk drops proportionally, and the rate charged should reflect that lower risk — not stay at the level of an operation with no guarantee at all.
How does a forensic audit recalculate the cost of an operation with assistance-spread capture?
The methodology consists of establishing the program's own average reference rate for comparable operations, recalculating the payment flow using a rate consistent with the bank's actual residual risk, and isolating ancillary charges billed without a corresponding service. In the cases reviewed, this resulted in total-cost reductions of roughly 25% to 40%.
What should I ask the bank before signing an FGI-PEAC or BNDES-guaranteed credit line?
Ask what share of the risk is covered by the guarantee fund, whether that coverage is already reflected in the offered rate, and compare the rate with the program's own reference parameter — not the free-market rate. Also add up every collateral layer required and question whether it makes sense given a risk the state has already reduced.
Dr. Lincoln Sposito
Specialist in banking audits and financial forensics, combining the statistical rigor of data science with the analysis of banking-system architectures to dismantle predatory charges against SMEs. Learn more about the expert →