The Segmint+Snowflake Partnership - Data and the Cloud

For most financial institutions, it’s impossible to truly understand consumer transaction behavior with the wide variance of merchant payment descriptions. We help you unlock the power of your merchant payment and transaction data by producing consistent and usable insights with our Merchant Payment Cleansing service.

Transaction cleansing is a critical tool that allows financial institutions to better understand customer transaction behavior and model spend patterns. The content of merchant payment transactions is often cryptic and non-descript, with a significant number of transaction variants for a single merchant, making it difficult to distill into a merchant name. The detailed transaction knowledge base and rules library are the key inputs to a machine learning and AI infrastructure that can accurately cleanse and enrich millions of transactions a day. Segmint’s technology takes each new raw transaction received and matches it to the most relevant human-verified entry in the knowledge base and enriches the transaction to identify a cleansed business name, categories and logos. This process is executed with tremendous speed and scale, a human touch is part of every single enrichment.

Segmint has seamlessly integrated our Merchant Payment Cleansing service into Snowflake's data cloud environment alleviating the need for clients to implement complex data pipelines to transfer data across platforms. The function to “call” the data within Snowflake to access the Merchant Payment Cleansing solution makes it incredibly simple, fast, granular, accurate, fully secure and compliant around the rules within the cloud and can be easily incorporated into your existing ETL/ELT/data prep workflows. The enriched transaction information provided by the MPC solution is granular, accurate, and will equip your organization with additional dimensions to generate new insights or to enhance current analytic models.

The Merchant Payment Cleansing Solution

Many financial institutions (FIs) are considering Merchant Payment Cleansing as a key ingredient to a data strategy. An FI can leverage this clean, tagged data to provide a more organized online banking experience, avoid transaction disputes by providing better online banking statements, and reduce IT man hours, lag time and quality issues.

Turn cryptic raw transaction strings like:

MURPHY6890ATWALMART 1468 BRINDLEE MOUN ARAB ALUS Card #
LITTLE ROSIES HUNTSVILLE ALUS Card #6403
MERCHANT PURCHASE TERMINAL 490641 JTV 161355552 4of5 800 55083 TN XXXXXXXXXXXX0117
MERCHANT PURCHASE TERMINAL ***MU PTS PAYSTATION COLUMBIA MO XXX ****

Into easy to understand merchant matches, typically accompanied by a brand logo:

  • Murphy Express Gas Station
  • Gas Stations
  • Transportation & Automotive
  • Rosie’s Mexican Cantina
  • Mexican Restaurants
  • Food & Dining
  • Jewelry Television
  • Jewelry Stores
  • General Merchandise & Shopping
  • University of Missouri
  • Colleges & Universities
  • Education

Through our pre-integrated access to complete core and transaction data, we’ve analyzed billions of transactions across more than 45,000 merchants and financial institutions.

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