Despite being extremely valuable in terms of consumer insights, raw transaction data is notoriously difficult to make use of.
The insights hidden in transaction data offer enormous opportunities. By analysing how customers spend, financial institutions can understand their needs both now and in the future, and build personal relationships with individual customers at scale through efficient digital services.
But why is the data itself so challenging to work with?
There are several factors contributing to the problem:
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.
gini 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 and location, 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, click the link below.
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