Categorisation is a crucial – yet often dangerously overlooked – element of any great digital bank. When done right, categorisation not only gives customers unique insights into their spending habits, but serves as a building block for many digital banking experiences.
However, designing the perfect categorisation model is more complex than it might seem. Too many categories can overwhelm users and render the feature unusable, while too few may not provide enough insights into their finances. Finding the right balance between accuracy and usability is key to ensure categorisation that is accurate, valuable, and engaging for the banking customer.
In this article, we will explore how to implement categorisation that is just right to maximise the ROI for the bank and increase banking customers' financial awareness.
What is Categorisation?
Historically, budgeting has always relied on some form of categorisation.
Before digital banking, many people used a hands-on approach like the Envelope System—physically dividing their cash into envelopes labelled with spending categories such as Rent, Groceries, and Entertainment. Once an envelope was empty, no more spending in that category was allowed!
Today, digital categorisation follows the same principle, helping customers allocate and track their spending seamlessly without needing paper envelopes or manual calculations.
Transaction Categorisation refers to the process of assigning financial transactions predetermined categories, such as Utilities, Restaurants, or Petrol, adding context to personal spending and empowering banking customers with better financial awareness.
Moreover, categorisation creates a foundation for effective financial management, allowing people to track their main expenses, engage in budgeting, and set financial goals. Monitoring how spending evolves over time and identifying opportunities to cut down on unnecessary costs all depends on the ability to see accurately where the money goes.
Creating the perfect category tree
When a financial institution decides to add categorisation to its digital bank, there are multiple aspects to consider. One of the most important decisions for a bank is determining the number of categories and the design of the category tree – the index to which financial transactions are mapped.
Imagine a customer logging into their digital bank with the aim of reviewing last month‘s spending and being presented with 20-30 categories crammed into a single pie chart.
This kind of overview is unlikely to give the consumer any kind of insight into his spending. Worse, it would probably overwhelm the consumer and drive him away from using the feature.
To avoid overwhelming their customers, most banks have adopted a tiered categorisation model, consisting of high-level “parent” categories and more granular subcategories.
The first tier should provide exactly enough information for the consumer to understand their monthly spending on a high level without going into too much detail.
These would be parent categories such as Food, Home, and Shopping & Services, with each category having a number of subcategories for added context.
A certain level of granularity is essential, especially when budgeting or setting a spending goal. If a person would like to spend less on Food, they might have difficulty cutting down Grocery expenses but might easily challenge themselves to spend less on Fast Food.
But where should banks draw the line on granularity?
Balancing Accuracy and Usability
Some banks might be tempted to provide even more granular categorisation to their customers, adding more tiers to their category tree. With advancing technology, banks could even analyse receipts to categorise Clothing & Accessories transactions down to micro-categories such as Sweaters, Pants, and Bracelets.
However, increased granularity raises the question of usability and value to the customer. While hyper-detailed categorisation might seem like a step forward, it can overwhelm users and make their financial overview harder to digest. Most customers don’t need to know exactly how much they spent on t-shirts versus jeans—they want a clear, intuitive overview of their spending habits.
Finding the perfect balance of usability and accuracy is what banks are striving for. Too many categories can lead to decision fatigue, reducing engagement and making budgeting tools less useful. On the other hand, overly broad categories may not provide enough insight for customers to make informed financial decisions.
Enhancing categorisation with Machine Learning
Our Enrichment solution consolidates raw financial data from multiple sources, both internally and open banking data, and enhances it with categorisation, merchant details, and other information.
Meniga determines categories based on results from multiple category detectors, such as MCC codes, texts, amount, internal bank codes, and enhances the accuracy by applying machine learning (ML) elements. Our intelligent categorisation learns from user contributions and community, steadily improving categorisation over time.
Meniga Categorisation is currently live across 30+ countries globally, with each category tree localised and created in collaboration with the corresponding bank to reflect its culture and customs.
With up to 95% categorisation accuracy, 80bn+ transactions categorised per year, and over 100 million banking customers worldwide benefitting from our solution, Meniga is the partner of choice for any financial institution.
Contact our team to find out how we can help you provide actionable, accurate, and engaging categorisation to your banking customers.
