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Merchant Category Codes (MCCs), initially designed as a universal standard for categorising transactions, have long been the baseline for transaction classification in banking. However, their limitations have created challenges that resonate across banking operations, customer experience, and sustainability initiatives.
This article pinpoints the critical flaws of MCC codes in modern banking, explores their role in analytics and sustainability, and highlights the transformative impact of enriched categorisation on personal financial management (PFM).
MCC codes are four-digit numbers used to classify merchants by the type of goods or services they offer. Payment processors, banks, and financial institutions rely on these codes to categorise transactions, track spending, and assess merchant activity. While they were designed to simplify transaction tracking, the system now struggles to meet the needs of a digital-first economy that values personalised services and much more accurate data.
Our earlier study revealed that MCC codes often provide too broad of a categorisation, which limits their utility for deeper financial insights. While MCC codes work in cases where they are universally applied across all merchants, they fail to meet the demands of personalised financial management, risk analysis, and sustainability efforts that are key pillars of modern digital banking.
Here are the key reasons why MCC codes are no longer a fit for today’s banking needs:
MCCs Are Too Broad: MCC codes categorise transactions under generic labels, such as "Dining," which fails to differentiate between casual coffee purchases and luxury dining experiences.
Did you know?
When comparing strictly MCC-based categorisation with more complex categorisation, which takes multiple factors into account, we discovered only 63 % of transactions were correctly categorised based on their MCC code.
MCCs Don’t Fit User Intent: The categories do not reflect the actual intent behind consumer spending (e.g. How consumer pays for the product or service). For instance, both casual dining and luxury restaurants are classified under the same dining category, making it difficult for customers to track their habits accurately. Recurring payments might also not be labeled correctly as a payment happening each month.
Did you know?
Subscriptions are becoming a common form of payment these days with customers paying regularly for media of all kinds. With this in mind, subscription tag becomes mandatory with regular MCCs unable to provide important context.
MCCs Miss Nuanced Consumer Behavior: As we saw with categories like "Fuel Dealers," there’s no differentiation between conventional gas stations and electric vehicle charging stations, missing an important piece of customer data in the age of green technologies.
These points are explored in greater detail in our study. If you’re interested in digging deeper into why these issues matter, you can explore the individual arguments linked to each of these flaws.
Modern categorisation systems, such as Tapix’s four-level framework, overcome these limitations by breaking down transaction data into several levels:
Now, let’s move from the technical details of MCC codes to the real-world consequences for users and banks. Here are three key use cases that demonstrate how the nuances of MCC codes impact the usability of banking apps:
For users trying to manage their finances effectively, MCC codes provide very limited insight into their spending patterns. Here’s a practical example:
Standard MCCs: Sarah, a young student, opens her banking app and sees her dining transactions categorised under a single "Dining" label. She has no idea whether she’s overspending on casual coffee trips or indulging in high-end restaurants. This lack of specificity leaves her confused about her spending habits and makes it harder for her to budget effectively.
Enhanced Categorisation: Thanks to transaction data enrichment, Sarah’s transactions are broken down into specific subcategories like "Dining – Coffee," "Dining – Fast Food," and "Dining – Fine Dining." With this clarity, Sarah notices she spends $150 monthly on coffee and $200 on fine dining. With this information in hand, she can set realistic goals, cut back where needed, and feel more in control of her finances.
The result? The app user feels more in control and uses the PFM platform more frequently to manage his finances. Studies indicate that consumers who have a clear understanding of their spending patterns tend to maintain higher account balances and engage in more frequent card transactions. Neontri discusses how enhanced transaction categorisation affects accuracy and efficiency of data, leading to better financial analysis and smart reporting for more efficient budgeting and financial management.
Banks rely heavily on transaction data for assessing risk, making lending decisions, and analysing customer behavior. The foundation of these processes is the transaction categorisation system in place. Traditionally, banks have depended on Merchant Category Codes (MCCs) to categorise payments, but these broad, high-level categories are increasingly proving to be insufficient for effective financial analysis.
Standard MCCs: When using MCC codes, spending patterns can be obscured, making it harder for banks to assess a customer's true financial situation. A person who spends $2,000 on electronics might seem like a high-risk borrower, but if this is a one-off purchase and not part of their regular spending habits, the MCC categorisation will fail to reflect this nuance. The result is either an unfairly high-risk score or an inability to detect anomalies that may indicate financial distress.
With Enhanced Categorisation: With more specific data, banks can develop more personalised credit scores. For instance, someone who frequently spends on "Dining – Fast Food" might be flagged for higher risk if they are spending excessively relative to their income, whereas someone whose spending is limited to "Dining – Fine Dining" may be seen as someone who has the discretionary income to support such expenses. By breaking down categories more effectively, banks can account for nuances in customer behavior that would otherwise be lost.
Not everything in raw transaction history is as it seems. This is why enhanced data provides banks with more options to understand user’s true behavior and assess the risk accurately. With that in mind, reaching 100% data coverage is nearly impossible for several reasons:
The ripple effects of enhanced categorisation extend beyond customer experience, directly influencing key performance indicators (KPIs) for banks. For example:
As environmental concerns continue to grow, businesses - especially financial institutions - are under increasing pressure to support sustainability initiatives. One of the most important aspects of this is helping consumers and businesses understand and track their carbon footprints. This is important not only for meeting regulatory standards but also for responding to the growing consumer demand for environmentally responsible products and services.
Did you know?
According to EY, 61% of consumers feel they need more information to make better choices when shopping sustainably, highlighting the need for education and resources to make eco-friendly decisions easier
Standard MCCs: MCC codes were never designed with sustainability in mind. They categorise transactions based on broad merchant types, but they fail to account for the environmental impact of consumer spending. The category "Fuel Dealers" doesn’t distinguish between high-emission fuel and clean energy alternatives, making it impossible for banks to track their customers' environmental impact accurately.
With Enhanced Categorisation: Banks can move beyond the limitations of MCC codes to offer a more detailed, accurate view of their customers’ environmental impact. By categorising transactions with more precision, banks can track specific types of purchases - such as differentiating between conventional fuel and low-emission energy sources like electricity for electric vehicles or renewable biofuels, or even different types of transportation altogether.
Small and Medium-sized Enterprises (SMEs) play an important role in the global economy, and their transition to sustainable practices is essential for broader environmental goals. However, many banks, despite their intentions, face challenges in effectively supporting SMEs on this journey.
A significant hurdle is the lack of tailored financial products that address the unique needs of SMEs. While large corporations often have access to substantial green financing options, SMEs may struggle to find suitable funding for their sustainability initiatives. Additionally, the complexity and administrative burden of new sustainable reporting rules can be overwhelming for smaller businesses, potentially slowing their progress toward greener practices.
Moreover, limited budgets, lack of expertise, and operational constraints often make it difficult for SMEs to adopt green practices effectively. Banks themselves can play a crucial role by providing not only financial support but also education and resources to help SMEs navigate the complexities of sustainability.
The result? By embracing enhanced categorisation and developing tailored support strategies, banks can align their offerings with sustainable finance initiatives, opening up new opportunities to support eco-conscious customers and SMEs. This approach meets regulatory and consumer demands while moving companies towards more sustainable economy, benefiting all stakeholders involved.
Ready to learn more?
Explore how Tapix can transform your transaction data.
MCC codes are limited by their broadness, but solutions like Tapix’s enhanced categorisation offer a much more granular and actionable approach. And the best part? These solutions come at no extra cost to banks while providing a much richer set of data points that can be used across a variety of use cases - from fraud detection and credit scoring to sustainability and PFM tools.
For more details on how these solutions can benefit your bank, explore the Tapix offerings.
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Michal Maliarov
Senior insider
