December 2001, when Europe was phasing out legacy currencies for the euro, the Belgian Consumer Organization Test-Achats found an unsettling trend. Merchants were routinely taking advantage of rounding errors in currency conversions. When Belgian Francs (BEF) were converted back to euros at the official exchange rate of 40.3399 BEF per euro, the obligatory rounding to a nearest cent caused tiny differences. When these rounded figures were then reconverted to francs for accounting records, the impact of original rounding built up, causing additional inaccuracies. The apparently innocuous rounding—a few cents one way and another—was allowing stores to make unfair gains, as shoppers were losing about 0.01 EUR on transactions of about 101 EUR.
The Belgian regulators saw this wasn't only about pennies; it was about trust. Research by Belgium's Price Observatory estimated that even in the worst-case scenario when prices were all rounded up, the consumer price index would rise by 0.54% to 0.72%. The moral was obvious: in financial systems that are processing millions of transactions, accuracy isn't a choice—it's essential to fairness.
Twenty-four years later, our team learned this lesson the hard way while working on an international client's cross-border payment project.
When we began building a cross-border payment gateway for one of our foreign fintech clients, they were in desperate need of getting at least an MVP out the door in a hurry. In hurrying to market, we made what at the time seemed a reasonable trade-off: we did all calculations using a centrally fixed conversion rate. Who would care about a few fractions of a cent, right?
The quoting engine we developed supported conversions in dozens of currencies, including mid-market rates, platform markups, and partner exchange rates. Every transaction included several math operations: retrieving real-time rates, applying percentage markups (usually 0.10%), determining partner spreads, and exchanging currency pairs. Throughout, we truncated values after four decimal places.
The architecture made sense. For our conversions between any two currencies—Bahraini Dinar (BHD) to Indian Rupee (INR), for example—we employed a hub-and-spoke model with the USD as the global go-between. The computation was simple:
Amount in Target Currency = Amount in Source Currency × CR(Source → USD) × CR(USD → Target)
Where CR stood for statically held conversion rates, all fixed relative to the US dollar. They were hardcoded into the system and never changed, as we assumed the partner rates and mid-market rates would adjust for any staleness in the base conversion factors.
The system worked—until it didn't.
So around six months after launch, our client received a complaint that found us all cold. A customer had requested a quote for converting BHD to INR and carefully compared the guaranteed rate with what they actually received. The discrepancy wasn't huge—about 0.8%—but it was consistent, and it was always in the platform's favor.
We originally wrote it off as a miscommunication. Maybe the customer was quoting prices at different times, or hadn't factored in the revealed markup. But when we did the math ourselves, the issue was undeniable: the platform was listing prices that differed from true costs, resulting in uncertain profit margins and sometimes shortchanging clients.
Let's look at an actual transaction: exchanging 1 BHD to INR. This is what was going on behind the scenes with our flawed system:
Step 1: Exchange BHD to USD using static rate
Step 2: Exchange USD to INR using static rate
Step 3: Apply partner rate adjustment
Step 4: Apply platform markup of 0.10%
But the actual rate should have been approximately 223.70 INR for 1 BHD based on real-time market conditions. While our calculation came close in this example, the problems compounded in different scenarios
It wasn't merely a technical issue. It posed an existential threat to our client's business. The platform was randomly pricing trades and opening them up to regulatory scrutiny and possible accusations of fraud.
For three weeks, our engineering team toiled around the clock trying to find a solution. We scoured industry standards, spoke with finance engineers, and reviewed how incumbent providers such as Wise and Stripe managed precision. We sought an approach that would
The epiphany happened on a Tuesday afternoon. I was going through cross-rate triangulation equations and comparing the partner rate versus the mid-market rate when a trend emerged. The partner always offered better rates than the mid-market—that's how they attracted clients. What if we could dynamically compute the conversion rate based on real-time mid-market rates as the base, then multiply it by the partner's premium, while subtracting the platform markup from the partner's spread?
Last night, I had understood that rounding at every step of calculation was suffocating our accuracy. The problem crystallized: do all the calculations with precision, and round only once at the end.
The formula that saved the day:
Final Rate = (MMR(Source → Target) / MMR(USD → Target)) × PR(USD → Target) × (1 - Platform Markup)
Where:
The elegance of this formula lies in its reasoning: it determines what amount of each source currency is worth one USD based on the ratio of mid-market rates, then multiplies that conversion by the partner's favorable rate, and finally deducts the platform markup from the partner's spread to make both the platform and the customer gain.
Let's go through an example converting 1 BHD to INR:
Given rates:
Calculation:
Step 1: Calculate the base ratio
Base ratio = 221.28 ÷ 83.50 = 2.6500
This informs us that 1 BHD is equal to 2.65 USD-equivalents according to prevailing market rates.
Step 2: Apply partner rate and platform markup
Final Rate = 2.6500 × 84.50 × (1 - 0.001)
Final Rate = 2.6500 × 84.50 × 0.999
Final Rate = 2.6500 × 84.4155
Final Rate = 223.70 INR per BHD
The profit mechanism:
All parties benefit: the customer receives a better rate than the mid-market (223.70 vs 221.28), the platform benefits from its 0.10% markup over the partner's spread (0.22 INR per BHD), and the partner benefits from their premium above mid-market rates.
This one formula removed several issues at once:
We implemented the new quoting engine in phases, operating both systems concurrently for two weeks to ensure accuracy. The outcome was exceptional:
Most importantly, we discovered what financial institutions have known for decades: financial calculation precision isn't about mathematical beauty—it's about customer trust and regulatory compliance.
Our experience has the following key lessons for anyone constructing financial systems:
1. Round late, not often
Do all in-between math with full precision (use full floating-point or decimal types) and round just at the end to the desired currency precision. Every rounding introduces error that accrues through calculations.
2. Never use static exchange rates for real-time transactions
Market rates can move 2-5% daily during volatility; static rates create arbitrage opportunities, pricing errors, and unpredictable margins. Invest in reliable real-time rate providers—the cost is negligible compared to the risk.
3. Eliminate unnecessary conversions
Each currency conversion introduces rounding error and computational overhead; calculate direct paths whenever possible. Hub-and-spoke models should only be used when absolutely necessary, never as a default architecture.
4. Utilize decimal types, not floats, for money
JavaScript, Python, and PHP should employ specialized decimal libraries (BigDecimal, Decimal, Money types) in order to eschew binary floating-point representation problems. Never use regular float or double types for fiscal calculations.
5. Store precision in excess of display needs
When displaying to 2 decimals, store no less than 4-6 decimals in order to be able to perform accurate recalculations and auditing. Storage is inexpensive; customer trust is invaluable.
6. Design for transparency and auditability
Each rate calculation should be traceable and explainable. You should know how you got to a quoted rate so that regulators and customers can see. Single-formula solutions are simpler to audit than multi-step transformations.
7. Test with real data at scale
Tiny rounding errors are undetectable in unit tests but disastrous on millions of transactions. Load test with production-scale volumes and check aggregate error remains within reasonable limits.
8. Understand your profit model
Your math should render it arithmetically impossible to lose money on a transaction (other than by fraud). If margins are random, your calculation logic is defective.
The 2001 Belgian franc to euro conversion crisis proved that ostensibly minor rounding choices can create systemic effects on consumers and markets. Two decades later, with infinitely more complicated financial products and greater volume, the stakes are even greater.
Working on this project taught us a fundamental truth: in fintech, precision isn't a technical detail—it's the foundation of trust. Every decimal place matters because behind every transaction is a person who trusts the platform with their money. That trust, once lost to sloppy mathematics, is nearly impossible to regain.
The afternoon I learned that formula wasn't simply solving an engineering equation. It was learning that financial technology, fundamentally, is about making promises. When a platform provides a rate, it's a promise. When math is accurate, that promise is fulfilled. And when promises are fulfilled, something greater than any profit margin is established: a reputation for dependability in a business where trust is paramount.
The Belgian Consumer Organisation knew it in 2001. Every successful fintech player knows it now. The only question is whether you'll learn it from reading history—or from doing it again.