Transaction Categorisation

Empower your users with personalised spending insights. Our machine learning models deliver accurate and tailored categories and tags, helping your clients understand their finances at a deeper level.

Transaction Categorisation

Expanding beyond MCC codes

Our merchant transaction categorisation system bypasses the use of traditional merchant category codes (MCC), leveraging instead advanced machine learning (ML) and artificial intelligence (AI) technologies.
By analysing individual merchant data in detail, the system assigns each business a precise category, along with additional contextual insights about the merchant’s activities.
This approach ensures a detailed, accurate, and nuanced understanding of transaction data and customer insights.

Effortlessly generate insights on brands by consolidating sub-brands expenditures

Flexible and comprehensive transaction categorisation system that provides a detailed and accurate reflection of spending behaviour

Simplified search and improved visibility with granular categories and attributes

For the end-user

Enhanced user experience:
Increase your customer engagement by providing tailored solutions.
Enable better financial management: Provide customers with a visual representation of their primary spending areas.
Provide better insights:
Enable your customers to better understand their finances, and explore trends and spending habits.

For the bank

Develop targeted products and offers:
Facilitate the development of specific and targeted products, offers, and solutions based on consumption habits.
Build brand loyalty:
And trust through personalised offerings and enhanced customer satisfaction.
Establish yourself as a digital leader:
By showcasing innovation and providing customers with enhanced financial insights and user-centric services.

Transaction Location

Add precise geo-enrichment data to each transaction.

Accurate Logos & Names

Empower your users to identify their transactions more easily.

Contact Details

Facilitate the resolution of unknown transaction queries.

Sustainability Insights

Foster more sustainable consumption habits.

How does categorisation work?

“Categorisation” is a way for banking apps to group transactional data within the app, giving clients and banks the opportunity to see where money is being spent. Usually, this is done using “Merchant Category Codes (MCCs)”, an approach that gives rudimentary data and is often mismatched to the product or service. MRS makes MCCs redundant by leveraging machine learning (ML) and artificial intelligence (AI) technologies. 


How does MRS enhance personal finance management?

MRS provides a clear and intuitive breakdown of transactions, which helps customers track their spending more effectively, identify potential savings areas, and adhere to their budgeting goals.


How can Snowdrop Solutions help?

Our MRS API takes it a step further and comes back with clean code that accurately matches the business’s name, location, and transaction category with a 95% success rate. We analyse individual merchant data in detail, assigning each business a precise category, along with additional contextual insights about the merchant’s activities. This approach ensures a detailed, accurate, and nuanced understanding of transaction data and customer insights.