Transaction categorisation — good vs. great

Jan 9 · 3 min read
Photo by Alex Radelich via Unsplash

The rise of Open Banking is ripe with opportunities for adopting new apps and solutions for analysing account data. Raw data on its own, however, doesn’t equal actionable insights. The missing ingredient enabling fintech solutions to enrich raw account data is a transaction categorisation engine.

Transaction categorisation is a process of identifying the context or purposes of specific bank account records. The process implies (1) using algorithms trained to recognise keywords, phrases or patterns in transaction details and (2) assigning categories based on pre-defined rules. But not all engines are created equal.

For developers or data scientists planning to use transaction categorisation for automated decision-making, it's important to choose the best engine for the task at hand. Having built one from scratch, here is our take on a good transaction categorisation engine vs. a great one.

Comparing categorisation engines

When comparing transaction categorisation engines, there are generally five factors that must be considered:

  1. Categorisation rate
    This is the percentage amount of how many transactions an engine is able to recognise. While it might seem to be the most important factor, it can in fact often be artificially “inflated”. It's possible to build a categorisation engine with a 95% categorisation rate, but this number alone does not reveal how many transactions were actually categorised correctly. To evaluate the true merit of a categorisation engine, it's important to look at the categorisation rate together with the error rate.

Good vs. great

A great transaction categorisation engine is one that has the right balance between the five factors to perform the task at hand.

For example, if you're building a personal finance management (PFM) app, you might require a fast categorisation engine with a reasonably high categorisation rate, a large number of categories, but having a low error rate is not critical — the app users wouldn't mind the results not being 100% correct. However, if you're planning to use transaction categorisation for credit risk purposes (e.g. automated credit decisions), having a low error rate is critical to ensure accurate credit decisions.

In conclusion, the rise of Open Banking has provided many opportunities to build incredible apps and solutions on account data. Transaction categorisation is a layer that adds 'superpowers' to these apps and solutions by enriching the raw account data. To best utilise these powers, it's paramount to find the best fit for your particular use-case.

If you're interested to see how your current transaction categorisation engine would perform against Nordigen's engine, let's talk:

About Nordigen:
Nordigen is a global account data analytics provider that helps banks and lenders improve the speed and accuracy of their credit decisions. We provide solutions that include income verification, verification of liabilities, transaction categorisation and behavioural feature engineering. For more information, please visit


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