On the morning of 1 August 2012, a US trading firm called Knight Capital switched on some new software. A piece of old code that should have been retired woke up and started sending orders nobody wanted. In about 45 minutes the firm lost roughly $440 million. It was sold within months.
Most software bugs are annoying. In finance, some of them end companies. A shopping app with a wrong photo gets a complaint. A payments app that sends a transfer twice gets a regulator and a lawsuit.
That is the background to a hiring trend that surprises people outside the industry. Standard Chartered, Barclays and younger companies such as Mercury have put Haskell, a language many developers only saw once at university, at the center of systems that move or value real money. If you have ever wondered why hire Haskell developers when Java and Python developers are so much easier to find, this article walks through the reasons, the trade-offs, and the places where the language actually earns its keep.
We will cover what Haskell is, how it compares with the usual alternatives, and then the daily mess of fintech work: missing data, feeds that disagree, split-second decisions and systems under pressure. By the end you should have a clear picture of why businesses are hiring Haskell developers for reliable functional software, and whether that choice makes sense for your own product.
First, what are we comparing?
When Haskell comes up in a fintech planning meeting, it is usually being weighed against Java or Python, the two languages most banks and startups already run on. So it helps to understand all three.
Haskell
Haskell first appeared in 1990, designed by researchers as an open language for a style called functional programming. It lived mostly in universities for years before moving into industry, especially finance and blockchain.
The easiest way to picture functional programming is a spreadsheet formula. If cell C1 says =A1+B1, it always gives the same answer for the same A1 and B1. It does not quietly change some other cell, send an email or remember what it did yesterday. Haskell programs are built out of small pieces that behave like that formula. Anything that touches the outside world, such as reading a database or calling a bank’s API, has to be clearly marked in the code.
Here are its main features in plain terms:
• A very strict type checker. Every value has a type, such as "an amount in rupees" or "an account ID", and the compiler checks that you never mix them up. If someone tries to add a phone number to a balance, the program simply will not build.
• Pure functions by default. A function takes inputs and returns an output. It cannot secretly change something elsewhere in the program. That makes it much easier to test and to reason about.
• Data that does not change. Once a value is created, it stays as it is. If a balance goes from 100 to 80, you get a new value of 80 and the old 100 still exists. Accountants will recognise this idea: you never rub out an entry, you add a new one.
• No surprise empty values. In many languages any value can turn out to be empty, which causes crashes. In Haskell, if something might be missing, its type says so, and the compiler makes you deal with the empty case.
• Good tools for doing many things at once. Haskell has very light threads and a feature called Software Transactional Memory (STM), which lets many tasks update shared data without stepping on each other.
• Lazy evaluation. Haskell only works out a value when something actually needs it. This can save work, but it can also cause memory problems if the team is careless. We will come back to this.
Where it fits best: systems where being correct matters more than shipping a demo by Friday. That means ledgers, payment engines, pricing and risk models, trading rules, smart contracts, compilers and small custom languages that a company builds for its own analysts. It is a weaker choice for quick prototypes, heavy front-end work, or apps that mostly glue popular third-party services together.
Java
Java has been the standard language of bank back offices since the late 1990s.
• It is built around objects, which are bundles of data plus the actions that change that data. This style is called object-oriented programming.
• It checks types before the program runs, though less strictly than Haskell. A value can still be null (empty) unless the developer is careful.
• It runs on the Java Virtual Machine, which is fast, mature and well understood by operations teams.
• It has a huge set of ready-made libraries, and millions of developers already know it.
Where it fits best: large enterprise systems, core banking platforms, Android apps, and big teams that need to hire quickly and move people between projects.
Python
Python is the friendly one. It reads almost like English and dominates data science and machine learning.
• It uses dynamic typing, which means types are only checked while the program is actually running.
• It has an enormous collection of libraries for data work, AI and automation, such as pandas and scikit-learn.
• It is easy to learn, so analysts and quants often write it themselves without waiting for engineers.
• Plain Python is slower than Java or Haskell for heavy number crunching, though libraries written in C cover much of that gap.
Where it fits best: data analysis, machine learning models, reports, scripts, research and prototypes. Many fintechs build a fraud model in Python and then hand the live decision to a stricter system.
How Haskell differs from Java and Python
None of these languages is simply "better". The real differences are about when mistakes get caught, and whether the developer or the customer finds them.
When errors are found
Python finds most mistakes while the program runs, which can mean in front of a customer. Java finds some before it runs, at compile time, but plenty still get through. Haskell moves much more of the checking to compile time. Haskell developers like to say "if it compiles, it usually works." That is an exaggeration, but not a big one for certain kinds of bugs, like mixing up two currencies or forgetting to handle a case.
Changing data versus creating new data
Java and Python let any part of a program change a shared value. In a large payments system, when a balance turns out wrong, engineers may have to search dozens of places that could have changed it. In Haskell, data is not changed in place, so the question "who changed this?" has a much shorter answer.
Hidden side effects
A side effect is anything a function does besides returning an answer, such as writing to a database, calling an API or sending a message. In Java or Python any function can do these things and nothing in its name or signature warns you. In Haskell, a function that does them carries a different type (usually called IO). Reading the code, you can see which parts are pure calculation and which parts touch the real world. Reviewers and auditors find that line very helpful.
Missing values
Tony Hoare, who introduced the null reference in 1965, later called it his "billion-dollar mistake". In Java an unexpected null causes a NullPointerException; in Python, None does the same. Haskell does not have a general null. It uses a type called Maybe, which says openly "this might be empty", and the compiler will not let you forget the empty case.
Doing many things at once
Java has threads and locks, and very mature tools for them, but locks are easy to get wrong. Standard Python has historically let only one thread run Python code at a time, although newer versions are starting to relax this, so teams often use several processes instead. Haskell gives you thousands of cheap threads and STM, which works a bit like a database transaction inside memory: a group of changes either all happen or none do.
Speed of building and hiring
Python wins on the first version; a prototype can take days. Java wins on hiring. Haskell starts slower with a smaller talent pool, and catches up later. When you change a type, the compiler lists every single place that needs updating, so large changes to old code are far less scary than they are in the other two.
Raw performance
All three are fast enough for most fintech work. Haskell compiles to native code through its main compiler, GHC, and for many server tasks it runs in the same range as Java and well ahead of plain Python. For microsecond-level trading, C++ still dominates, largely because memory clean-up pauses are hard to control in Haskell and Java.
Summary of the differences
Here is the same comparison in one place.
Why money software is a special case
Finance has three extra pressures. Every error has a direct price, because a wrong number is money in the wrong account. The rules are precise, written down and always changing. And everything must be explainable later: a regulator may ask in three years why a payment was blocked, and "the code did it" is not an answer.
The everyday benefits of Haskell development map neatly onto those three pressures.
Money gets its own type. A classic beginner bug is storing money as a floating-point number. In most languages, 0.1 + 0.2 comes out as 0.30000000000000004. Over millions of transactions those tiny errors add up. Haskell teams usually define a Money type that stores whole units of the smallest coin (paise, cents) along with the currency. The compiler then refuses to add dollars to rupees unless someone writes an explicit conversion.
data Currency = INR | USD | EUR
-- amount is kept in the smallest unit, e.g. paise or cents
data Money = Money Currency Integer
addMoney :: Money -> Money -> Either String Money
addMoney (Money c1 a) (Money c2 b)
| c1 == c2 = Right (Money c1 (a + b))
| otherwise = Left "Cannot add different currencies"
In plain words: the function returns either a proper sum or a clear refusal, and the caller must handle both.
Business rules become readable code. Because Haskell code is close to plain logic, a team can build a small custom language inside it for describing financial products. Analysts write product definitions in that language, and the Haskell compiler checks them. Barclays did exactly this with a system for exotic equity derivatives.
Results can be replayed. A pure function gives the same answer for the same input every time. So if you store the inputs, you can rerun a day of pricing or fee calculations months later and get the exact same numbers. That is very useful when auditors come calling.
Who is actually using Haskell in finance
Standard Chartered is the clearest example of scale. In a 2024 research paper, its engineers described Haskell supporting a Markets business that earned about $3 billion in operating income in 2023. Thousands of staff use the resulting software, and more than a hundred write functional code. A few years earlier, the bank was reported to have around half a million lines of Haskell and about 4.5 million lines of Mu. Some financial modellers and traders there write Mu themselves.
Mercury is interesting for a different reason. It is a young company whose founders came from a Ruby on Rails background, yet they picked Haskell on purpose and now run a couple of million lines of it in production.
The messy middle: real fintech problems and how Haskell handles them
Demos are clean. Production is not. Money systems spend most of their life dealing with half-broken inputs, late data and cases nobody planned for.
Data gaps
A bank transfer arrives without a reference number. A customer’s KYC record is missing a date of birth. The exchange-rate feed skipped a minute. These gaps happen every day.
In a loosely typed program, a missing field often becomes None or null and travels quietly through the system. Sometimes it crashes something far away. Worse, sometimes it is treated as zero and produces a normal-looking number. A loan approved because an unknown debt figure was read as "no debt" is a real kind of failure.
In Haskell, a field that might be missing is typed as Maybe. Any code that wants to use it has to say what happens when it is absent. Reject the payment? Park it for manual review? Use the last known exchange rate and flag the result? The important thing is that the decision is written down in the code, where a reviewer can see it and argue about it, instead of being an accident.
Conflicting signals
Two price feeds disagree by a few basis points. The partner bank says a payment has settled, but your own ledger still says pending. The fraud model says a transaction looks risky, while the customer has ten years of clean history. Real systems see conflicts like these all the time.
A common bug is code that simply trusts whichever message arrived last. Haskell teams tend to name every possible state up front using what is called a sum type, which is just a list of all the shapes a value can take:
data ReconResult
= Matched
| OnlyInBankFeed
| OnlyInOurLedger
| AmountMismatch Money Money
With the right compiler warnings switched on (most fintech teams treat these warnings as errors), any part of the code that handles a ReconResult must cover all four cases. If someone later adds a fifth case, say "DuplicateInBankFeed", the compiler points at every place that has not been updated yet. Nothing gets silently ignored.
Real-time decisions
A card payment has to be approved or declined in a fraction of a second. In that window the system checks the balance, the spending limit, the fraud score and maybe a merchant rule, all at once. Meanwhile a second payment for the same account might arrive at the same moment.
That second payment is where double spending comes from. Two requests both read "balance: ₹500", both approve a ₹400 purchase, and the account ends up negative. STM helps here. You wrap the read-check-update steps in one transaction. If another task changes the balance halfway through, the transaction is thrown away and retried automatically. Think of it as two cashiers who can never both grab the last note from the same drawer.
To be fair, Haskell handles millisecond decisions like card authorisation well, but it is not the usual choice for microsecond high-frequency trading.
Exceptions and edge cases
Money systems are full of cases that look rare but happen daily at scale. A refund that includes fees. A partial capture on a hotel booking. A chargeback four months later. A reversal of a reversal. A bank holiday that delays settlement. Interest maths that breaks on 29 February.
Haskell helps in two ways. The type system forces the team to list these cases explicitly, as we saw above. And Haskell gave the world a testing style called property-based testing, through a tool named QuickCheck. Instead of writing ten hand-picked examples, you write a rule, for example "after any transfer, the total money across all accounts is the same", and the tool generates thousands of random scenarios trying to break it. It is very good at finding the weird combination nobody thought of.
How the system behaves under load
Pure functions are easy to run in parallel because they do not fight over shared data. That helps with big batch jobs like month-end interest runs.
There is one trap, and it comes from lazy evaluation. If code keeps a running total over millions of transactions without calculating it as it goes, Haskell may store millions of "add this later" notes in memory. This is called a space leak. Experienced developers know the fixes, such as strict data and strict folds, and use GHC’s profiling tools to catch it early. This is one reason production experience matters so much when hiring.
Retries are another pressure point. When a call to a partner bank times out, the system retries, and it must never charge twice. Teams handle this with an idempotency key, a unique ID for each request. In Haskell you can make the "charge card" function impossible to call without one, so the protection cannot be forgotten by a new team member in a hurry.
What a Haskell developer actually does on a fintech team
The real job looks a lot like any backend role, with more time spent on design up front.
Say the product team wants a new business loan with a grace period. The developer first writes down the types: what a loan is, which states it can be in, and which events move it between them. Then they sit with compliance and turn the policy document into rules, asking questions wherever it is vague. After that come the logic, the property tests and the API endpoints, often built with a library such as Servant on top of PostgreSQL. Code review tends to focus on naming and clarity, because the compiler has already caught the crashes.
The key skill is careful modelling of the business before writing logic, which is exactly what finance needs.
The honest downsides
Any fair look at the benefits of Haskell development has to cover the costs too. Here they are without the sales gloss.
• A small hiring pool. Haskell developers are far fewer than Java or Python ones, and senior people cost more.
• A steep learning curve. Ideas like monads take time to click. A strong developer usually needs a few months to feel comfortable.
• Slow builds on large codebases. Big Haskell shops invest real effort in build tooling and caching.
• Fewer ready-made libraries. Payment providers ship SDKs for Java and Python first, so Haskell teams often write their own thin wrappers.
• Memory surprises from laziness. Covered above. Manageable, but only if someone on the team knows what to look for.
• Over-clever code. Haskell allows very abstract code. Teams need a style guide that favours plain, boring code.
Companies deal with these in fairly predictable ways. They train people in-house instead of waiting for perfect candidates. They hire strong general engineers and teach them Haskell, which Mercury is known for doing. They keep Haskell for the core where correctness matters and use other languages at the edges, such as Python for data science or TypeScript for the web front end.
Is Haskell a good fit for your fintech product?
A quick way to decide. Haskell is likely a good fit if:
• Your product has a core of complex money rules, such as a ledger, a lending engine, a pricing model or a payments router.
• A single wrong number could cost real money or trigger a compliance issue.
• You expect the rules to keep changing for years, so safe refactoring is worth a lot.
• You can accept a slower start for fewer incidents later.
It is probably not the right call if:
• You need an MVP in six weeks to test whether anyone wants the product at all.
• Most of your work is connecting third-party services with little logic of your own.
• Your small team has no functional programming experience and no time to train.
• Your main product is a data science or machine learning model, where Python’s libraries are hard to beat.
Many companies land in the middle: they build version one in whatever the team knows, then move the riskiest part into Haskell once the product has proven itself.
How to hire Haskell developers without getting burned
So, back to the original question: why hire Haskell developers? Because when the core of your product is money, you want the people and the tools that catch mistakes early. Here is how teams go about it in practice.
Look for production experience, not just enthusiasm. Many people learned Haskell as a hobby. That is a good sign, but ask what they have run in production, how they debugged it, and what broke.
Ask practical questions. How would you find a space leak? When would you use STM? How do you stop a retried payment from charging twice? Good candidates answer with stories, not textbook definitions.
Give a small, realistic task. "Build a tiny ledger that rejects unbalanced entries" tells you more than algorithm puzzles.
Widen the net, but keep a senior core. Developers from OCaml, Scala, F# or Rust often pick up Haskell quickly. One or two senior Haskell engineers can then guide several who are still learning.
Consider outside help for the first stretch. If you do not have Haskell experience in-house, a specialist development team can set up the architecture, coding standards and build tooling, then train your own engineers to take over.
The bottom line for fintech teams
Haskell is not a magic fix. It will not rescue a weak product, and it will slow down a small team that has never used it.
What it offers is a different trade. You spend more effort up front describing your money rules precisely, and in return the compiler guards those rules every time someone touches the code. For a ledger that has to be right on every one of millions of entries, or a payment engine that must never charge twice, that trade often pays for itself many times over. That, in short, is why businesses are hiring Haskell developers for reliable functional software: in finance, being right on the first run is cheaper than any fix afterwards.
If you are weighing the benefits of Haskell development against the cost of hiring, start small. Pick the one part of your system where mistakes hurt most, build it in Haskell with experienced people, and measure the results for yourself.
