Data entry ops · Production

super.AI disambiguates merchant names at 99%+ accuracy for global payment network operator

The problem

One of the world's largest payment network operators struggled to resolve merchant names from unclear billing descriptions at scale, facing constantly changing merchant data, tens of millions of merchants, hundreds of billions of transactions annually, and data obscured by aggregators and third-party payment solutions.

Workflow diagram · grounded in source
1
Unclear billing descriptor received
trigger
“struggling to resolve edge cases when attempting to resolve merchant names from unclear billing descriptions”
2
AI classifies billing descriptor
ai_action
“AI was used to classify unknown billing descriptors and improve the accuracy of the merchants mapped to the company's ever-growing and changing merchant list”
3
Merchant name disambiguated
output
“99% accurate merchant name disambiguation, including precise mapping to a single merchant even for duplicate entries—all with zero coding”
Reported outcome

The company achieved 99%+ accurate merchant name disambiguation with 99.7% process automation, processing up to 8.3M records hourly with zero coding required.

Reported metrics
Data accuracy99%+
Records processed hourly8.3M
Process automation99.7%
Time to first stage processing output3 weeks
Source
https://super.ai/case-studies/merchant-name-disambiguation
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

The company achieved 99%+ accurate merchant name disambiguation with 99.7% process automation, processing up to 8.3M records hourly with zero coding required.

What results were reported?

Data accuracy: 99%+; Records processed hourly: 8.3M; Process automation: 99.7%; Time to first stage processing output: 3 weeks (source-reported, not independently verified).

How is this data entry ops AI workflow structured?

Unclear billing descriptor received → AI classifies billing descriptor → Merchant name disambiguated.

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