Use Cases

Uncover the meaning behind transactions

Eliminate confusing strings of random letters and numbers on billing statements by using artificial intelligence to automatically clean merchant names. By transforming unclear merchant names into easily recognizable transactions, banks and payment network operators can improve customer experience, reduce customer service inquiries, and lower the number of disputed payments.

Take control over messy transaction data

Streamline and expedite merchant name cleaning for card issuers and payment network operators

It is common for charges on bank statements to show billing descriptors that are confusing or completely unrecognizable. While some merchants use clear names, many (especially small businesses) simply don’t.

Leverage AI to identify hidden patterns buried in unstructured billing descriptor data, then automatically transform unrecognizable merchant names into clear transaction records. Both card issuers and cardholders benefit from having easily recognizable merchant names attached to each transaction.


Data accuracy

Platform Success Story

Benefits of AI-powered transaction data enhancement

Fewer Customer Service Inquiries
Save time and resources by reducing customer service calls about unrecognized charges.
Clearer Billing Statements
Help cardholders better understand their transactions when reviewing bank statements.
Lower Operating Costs
Reduce operating costs thanks to fewer customer services inquiries and payment disputes.

Transaction data enhancement made easy

Super.AI's approach to transaction data enhancement in three easy steps.


Anonymized transaction data is uploaded via API to super.AI’s Unstructured Data Processing (UDP) Platform in a comma-separated values (CSV) format.

Super.AI considers security a core functional requirement for protecting customer data from accidental or deliberate theft, leakage, integrity compromise, and deletion. For more information about our data and security practices, download our Data Security Whitepaper.


Similarity search is used to identify and group items that can be resolved in batches versus outliers that require specific attention.

The pre-processed dataset is then sent to a model that attempts to resolve items automatically using a standard merchant database. Items that cannot be processed with AI are pre-labeled then routed to human workers for review.


Disambiguated merchant descriptors are used to update the merchant database. New transactions can then have their billing descriptors resolved using the more accurate data, and unrecognizable descriptors can be grouped and routed to super.AI for processing.

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