Transaction Categorization &
Merchant Identification for Turkey
Powered by machine learning, Gini’s TUBITAK (Scientific and Technological Research Council of Turkey) funded enrichment engine automates the data preparation process for banks, categorizes transactions, and adds merchant-level context to millions of raw transactions in minutes.
With an 88% accuracy ratio, your customers can see categorized transactions dramatically improving customer experience, reducing chargeback costs, and providing a much more personalized banking experience.
TESSERACT: Transaction Categorization & Merchant Identification Engine
The problem of unintelligible transaction data is costing the finance industry millions every year in customer support and chargeback and fraud investigations.
It’s also preventing banks from harnessing the power of big data analytics to deepen relationships and attract new customers, which could boost profits by 20% to 40%, according to a report by McKinsey & Company.
Tesseract uses machine learning to transform unintelligible raw transaction data into clean datasets rich in consumer intelligence. Instead of a string of numbers and letters, banks get an accurate merchant trading name, category, along with descriptive tags. This allows for much more efficient analytics and much richer consumer insights.
To find out more about how we help financial institutions harness the full potential of their transaction data, fill out the form on the right.