We’ve covered transaction enrichment in detail before - how raw, chaotic payment data gets cleaned up and made meaningful. But this time, we’re looking at it from a different perspective. If you’re planning to integrate an enrichment API into your business or just want to know how API works in the context of digital banking, this article will be your guide.
Why is Transaction Data Enrichment Important
First, let's quickly recap what an API is. To put it simply - it’s structured, purpose-built access to an up-to-date, ever-evolving database. It lets banks, fintechs, or developers send raw transaction data to an API - like merchant names, timestamps, and amounts - which then returns cleaner, more useful information. And why even bother with enrichment? The core reason, besides a fancy logo next to a transaction that makes sense, is about clarity. And making people feel like their money is under control - even if their spending habits aren’t.
You’re cleaning up data and helping people understand how they live their financial lives. But the problems can surface long before you get your hands on an API.
Shifting Right Instead of Shifting Left
In software development and DevOps, the concept of “shifting” refers to two different approaches to the same situation. Shifting left means addressing problems as early in the process as possible - closer to the source of the data or issue - rather than dealing with them downstream. In the context of transaction enrichment APIs, this mindset is especially critical.
Too often, product and data teams “shift right” without realising it. They bring in features and services without properly sorting and stabilising the underlying data first. That’s where the transaction data enrichment API comes in. Feed it raw information and get high quality and accurate transaction data. Just remember, enrichment APIs are only as good as the data they receive. We like to call it “garbage in – garbage out”.
In digital banking, where the transaction stream is constant, messy, and high-volume, shifting left means solving problems before they hit the API. It means thinking of enrichment not as a downstream task, but as part of your core data pipeline. And it can significantly improve the speed and consistency of everything. The key is to normalise transaction inputs before sending them, build validation at the source and create QA on the entire data stream, not just the output.
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How Banks Apply These Principles
Treating enrichment as something to "fix later" usually ends in technical debt. Mature digital banks and fintechs know this, and they’ve adapted accordingly.
Take Revolut, for example. They've been doing transaction enrichment since 2016, starting with internal processes. From the beginning, they understood that clarity and context in transaction data were essential to user trust and satisfaction. After moving to external enrichment, they shifted left early - investing in data normalisation and structured ingestion - and as a result, built enrichment into the DNA of their product.
bunq followed a similar path. They started with internal enrichment, managing their own mapping logic and classification infrastructure. But as the complexity grew - new merchant terminals, localisation challenges, growing user expectations - they switched to an external enrichment provider. Why? Because maintaining global coverage, low latency, and constant merchant updates in-house simply wasn’t scalable. It diverted focus from their core product mission.
Then there's Partners Banka, a modern digital bank that integrated enrichment APIs from day one as part of its tech stack. Rather than trying to build their own solution, they opted for a modular, external-first approach - ensuring consistent outputs, faster time-to-market, and reduced QA overhead.
The main question remains – should you stick with an in-house solution or go for an external service provider?
Building your own enrichment engine might seem appealing. More control, tighter integration, potentially lower costs. But in reality, you're signing up for a constant battle with:
- Terminal inconsistencies
- Merchant rebrands
- Regional edge cases
- QA debt and model drift
- Ongoing feedback loops and retraining
Unless data enrichment is your core product, it's likely not worth it. External APIs like Tapix already handle the heavy lifting:
- Continuous merchant updates
- Global coverage with fallback logic
- Manual data check with supporting AI algorithms
- Feedback APIs to let users report issues in real-time
Best of all, they let your team focus on building great experiences, not firefighting merchant mapping issues.
The Usual Mistakes and How to Avoid Them
Enrichment is a behind-the-scenes task that feels simple - until it quietly unravels across production environments, support tickets, and confused users. Let’s unpack some of the most common missteps and what to do instead.
Sending Poor-Quality Data Into the API
Banks often push raw, inconsistent, or chaotic transaction data into enrichment APIs - expecting the system to fix everything. Merchant strings are clipped, fields are misaligned, key parameters (like mcc, country, merchant ID) are missing entirely.
Why it matters:
Like we said earlier – Garbage in means garbage out.
No enrichment engine can compensate for missing or malformed input. When key parameters are absent, the drop in coverage can be massive. With proper inputs, brand recognition can reach 85%. But remove the mcc, country or merchantId, and category recognition could drop to 50% or lower.
How to fix it:
- Clean and normalise all input data before sending it - standardise field formats, extract separate location values (city, zip, country), and fix truncation or spacing issues.
- Set up validation rules in your pipeline that flag malformed or incomplete transactions before they hit the enrichment layer.
- Make sure your tech team understands which fields are critical: description, merchantId, country, and mcc should not be optional. They’re your keys to brand, category, and location coverage.
- Talk to your provider during integration. To ensure accurate and high-value enrichment results, we implement data quality control during this phase - before any data is sent to the API.
Missing Duplicate Transactions
APIs are often called multiple times for the same transaction. Maybe it’s a webhook retry, maybe a scheduled batch job ran twice, or maybe it’s just some subtle backend misconfiguration. Whatever the reason, the result is the same - duplicate calls to your enrichment API.
Why it matters:
To be clear, duplicate transactions mean all available parameters are exactly the same - same merchant name, same amount, same card ID, same everything.
But if the “transactionTimestamp” differs, it’s not technically a duplicate. It could be a legitimate repeat purchase, a retry at a POS terminal, or even a user tapping twice. Timestamp is your anchor - it’s how you distinguish between a true duplicate and a new, but nearly identical, event.
How to fix it:
- Use idempotency keys wherever possible when calling your enrichment provider - this helps avoid accidental repeats during network retries or async jobs.
- Implement de-duplication logic on your side using a smart composite key (e.g., userID + merchant + amount + transactionTimestamp) to detect true duplicates.
- Respect “transactionTimestamp” as the differentiator - it’s your best bet for distinguishing same-amount, same-merchant purchases from actual repeats.
Relying Only on Output-Level QA
You only check what the API returns - merchant name, logo, or category - without ever auditing the incoming transaction stream.
Why it matters:
The enrichment API is only as good as the data it receives. You may miss upstream issues like null fields, inconsistent casing, or strange terminal strings - leading to poor outputs.
How to fix it:
- Add QA gates at ingestion: Are merchant names complete? Are location fields consistent?
- Monitor key health metrics in your pipeline (e.g., % of transactions missing metadata).
- Ask your enrichment partner for tools to flag and fix upstream anomalies.
Skipping Feedback Loops
You assume the API output is final and don’t let users report bad merchant names, logos, or categories.
Why it matters:
Your customers are a free QA army. If you ignore their feedback, you miss real-world correction opportunities - and your API data goes stale.
How to fix it:
- Implement user-facing correction tools in your app (e.g., “Wrong logo? Report here”).
- Push that feedback into your enrichment provider via Feedback APIs (Tapix supports this).
- Prioritise feedback-rich merchants like recurring subscriptions, food delivery, and multi-brand franchises.
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What Are Real API Metrics?
Even though it sounds appealing, 100% merchant coverage is a myth, since there are many variables you have to consider. Some merchants prefer anonymity (e.g. Pornhub shows as “HJK78KF2” or “GYM MEMBERSHIP”), many terminal setups are inconsistent (switching a terminal that was used in McDonalds to Zara is a real thing).
Some POS terminals only process a handful of transactions - enriching them isn’t worth the cost. Statistically, 25% of terminals generate 99% of transactions.
So instead of chasing perfection, prioritise three core pillars – accuracy, coverage and data richness.
Treat Enrichment Like a Product
We’ve worked with 50+ banks, fintechs, neobanks, and budget apps - and the pattern is always the same: teams that treat enrichment as an active, evolving product (with feedback loops, QA, user input, and iteration) always get better results.
Transaction enrichment is more than a data layer - it’s an extension of your user experience. And when it’s done right, it feels seamless and effortless.
For more details on how enrichment solutions can benefit your bank, explore the Tapix offerings.
