MCC Codes vs AI Transaction Categorization: Which Approach Delivers Better Merchant Categorization?
Discover where MCC codes fall short and how AI-powered transaction categorisation can improve merchant accuracy, context and spending insights.
- Published
For decades, Merchant Category Codes (MCCs) have been the foundation of transaction categorisation across the payments industry. Card schemes, acquiring banks and financial institutions rely on them to classify merchants, power rewards programmes and support fraud detection.
However, customer expectations have changed. Today's banking users expect far more than a generic transaction category. They want to know exactly where they spent their money, what they purchased and how each transaction fits into their spending habits.
This shift has exposed the limitations of relying on MCC codes alone. While they remain an important part of the payments ecosystem, they were never designed to provide the level of merchant intelligence required for modern digital banking experiences.
AI-powered transaction categorisation addresses these gaps by combining merchant intelligence, contextual information and machine learning to deliver richer, more accurate transaction data.
In this guide, we'll explore where MCC codes remain valuable, where they fall short, and why many financial institutions are combining them with AI-driven transaction enrichment.
What Are MCC Codes?
Merchant Category Codes (MCCs) are four-digit numbers assigned to merchants by payment networks and acquiring banks. Each code represents the primary type of business a merchant operates.
For example, supermarkets, restaurants, petrol stations and airlines each have their own MCC ranges. These codes help payment networks process transactions and support functions such as interchange fees, rewards programmes and fraud monitoring.
Because MCCs follow industry standards, they provide a common language across the payments ecosystem. This makes them an effective starting point for classifying merchants, but they only describe a merchant's broad business category rather than the specific context of an individual transaction.
Why Banks Still Use MCC Codes
Despite advances in AI, MCC codes continue to play an important role within banking and payments.
For these reasons, MCC codes remain valuable operational data. The challenge is that they were designed for payment processing, not for delivering customer-friendly transaction experiences.
| Benefit | Why it matters |
|---|---|
| Industry standard | Supported across card schemes and financial institutions worldwide |
| Simple implementation | Easy to integrate into existing payment systems |
| Fraud detection | Helps identify transactions that fall outside a customer's normal spending behaviour |
| Rewards programmes | Enables cashback, loyalty schemes and category-based incentives |
| Baseline categorization | Provides an initial classification when richer merchant information is unavailable |
The limitations of MCC codes
As digital banking has evolved, the limitations of MCC-based categorisation have become increasingly apparent.
Incorrect MCC Assignment
One of the biggest challenges is that banks and acquiring institutions are responsible for assigning Merchant Category Codes. If a merchant receives an incorrect MCC, every downstream system that relies solely on that code inherits the same classification.
This means that even when the merchant has been correctly identified, the transaction can still appear under the wrong spending category.
Limited Merchant Context
MCC codes classify businesses at the merchant level rather than the transaction level.
Take Marks & Spencer as an example. A customer could purchase groceries, clothing, beauty products or enjoy a coffee in one of its cafés. Regardless of what was actually purchased, every transaction is associated with the same merchant category.
As banks look to provide meaningful spending insights, this lack of context becomes increasingly limiting.
Country-Specific Variations
Although MCCs are based on industry standards, implementation is not always consistent across markets.
Some financial institutions maintain their own extended or customised MCC mappings, while others interpret categories differently. This creates inconsistencies for banks operating across multiple countries and makes global transaction categorisation more challenging.
Overly Broad Categories
Certain Merchant Category Codes are simply too generic to provide meaningful customer insights.
Examples include:
- 7299 – Miscellaneous Personal Services
- 8999 – Professional Services, Not Elsewhere Classified
While these categories satisfy payment processing requirements, they offer little value when customers want to understand where their money has actually gone.
Static Classification
Modern merchants rarely fit neatly into a single category.
Marketplaces, delivery platforms and retailers continuously expand their services. Amazon, for example, sells everything from books and electronics to groceries and home insurance.
An MCC remains static, while businesses evolve constantly.
Why AI Enhances Transaction Categorisation
Rather than relying on a single four-digit code, AI-powered transaction categorisation combines multiple sources of merchant intelligence to build a more complete picture of every transaction.
Merchants adapt their services over time while often keeping the same point-of-sale configuration and MCC assignment. These changes are publicly visible and highly dynamic across the internet, but MCC codes remain static. AI-based systems are able to capture this evolving merchant reality and reflect it in transaction categorisation.
Instead of asking "What category does this merchant belong to?", AI asks "What do we know about this merchant from multiple trusted sources?"
This results in more accurate categorisation and significantly richer customer experiences.
AI Uses Multiple Signals
Modern transaction enrichment platforms can combine information such as:
- Merchant name
- Merchant location
- Merchant databases
- Historical transaction patterns
- Business descriptions
- Payment metadata
- Merchant logos and verified business information
Looking at multiple signals rather than a single code enables AI to identify merchants more accurately, even when raw transaction data is incomplete or inconsistent.
AI Reduces Ambiguity
Raw transaction strings are often cryptic.
For example:
AMZN Mktp UK PMTS
Without additional context, this provides little useful information.
A traditional MCC might simply classify the transaction as general retail.
An AI-powered enrichment platform can identify Amazon as the merchant, recognise its verified brand, display the merchant logo, provide location information where appropriate and categorise the purchase with much greater confidence.
AI Learns From Real Merchant Data
One of the biggest advantages of AI-based categorisation is that models are trained using large volumes of real merchant data rather than relying exclusively on predefined code lists.
As new merchants emerge and businesses evolve, AI models can adapt more quickly than static classification systems.
When combined with continuously validated merchant intelligence, this approach reduces ambiguity and improves categorisation consistency across countries and merchant types.
MCC Codes vs AI Transaction Categorization
| Capability | MCC Codes | AI-Based Categorisation |
|---|---|---|
| Merchant identification | Basic | High accuracy using multiple data sources |
| Merchant context | Limited | Rich merchant intelligence |
| Supports evolving business models | Limited | Yes |
| Handles ambiguous merchants | Limited | Strong |
| Learns from new merchant data | No | Yes |
| Global consistency | Varies between institutions | Improved through centralised merchant intelligence |
| Customers spending insights | Basic | Detailed and personalised |
| Expense management | Limited | Significantly improved |
Should Banks Replace MCC Codes?
Not entirely.
MCC codes continue to serve important operational purposes across payment processing, rewards programmes and fraud monitoring.
However, using them as the primary source of transaction categorisation no longer provides the level of accuracy that customers expect from modern banking applications. In many cases, the issue is not that MCCs are “wrong”, but that they are too broad or ambiguous to capture the true nature of a transaction.
The strongest approach combines both technologies.
AI can identify merchants, resolve ambiguous transaction data and generate meaningful spending categories, while MCC codes can still act as an additional validation signal where appropriate.
Rather than replacing MCC codes, AI makes them more useful by placing them within a broader merchant intelligence framework.
How Snowdrop Improves Transaction Categorisation
At Snowdrop, we go beyond static classification by capturing the dynamic nature of modern merchants. As businesses evolve, expand their services, or shift their point-of-sale configurations, these changes are often visible and continuously updated across the internet.
Our Merchant Reconciliation System (MRS) API is designed to detect and incorporate this merchant dynamism in real time, ensuring that categorisation stays aligned with how merchants actually operate today.
Instead of relying solely on generic AI models, our categorisation approach has been refined over many years through collaboration with banks, payment providers and industry partners. This continuously evolving merchant taxonomy helps reduce ambiguity while improving consistency across millions of transactions.
The result is enriched transaction data that includes:
- Clean merchant names
- Merchant logos
- Consistent spending categories
- Customisable categories for specific customers or internal processes
- Merchant locations powered by Google Maps
- Additional merchant information where available
- Ability to customise categories
MCC codes remain part of the process, but they are treated as one source of validation rather than the primary driver of categorisation.
This enables banks to deliver clearer transaction histories, more accurate spending insights and a better overall customer experience.
Frequently Asked Questions
Are MCC codes enough for transaction categorisation?
No. MCC codes provide a useful starting point, but they lack the merchant context needed for accurate spending insights. Modern transaction categorisation typically combines MCCs with merchant intelligence and AI.
Is AI more accurate than MCC codes?
AI generally delivers higher categorisation accuracy because it analyses multiple sources of information rather than relying on a single merchant category code. This helps reduce ambiguity and improve merchant identification.
Should banks stop using MCC codes?
No. MCC codes remain valuable for payment processing, fraud detection and rewards programmes. The best results come from combining MCC validation with AI-powered transaction enrichment.
How does AI improve expense management?
Accurate merchant categorisation gives customers clearer spending insights, improves budgeting tools, makes transaction searches more reliable and enables banks to deliver more personalised financial experiences.
