Merchants Categorised
Categories
Sub-categories
Bank transaction strings are notoriously hard to understand, as they are strewn with various alphanumeric characters, truncated merchant names and multiple delimiters. Every financial institution has its own syntax, making transactions more messier and inconsistent. The financial transaction categorisation process has never been easier, requiring a lot of laborious effort to identify people’s financial behaviour akin to analysing DNA containing all affirmative financial acts that define a person.
WIth our Transaction Categorisation and Enrichment API, Transaction data is deeply entrenched in multiple processes, even to the level of analysing users’ financial behaviours and spending patterns. Hence it helps Financial Institutions in understanding and defining the users they interact with. Harness the power of personal and financial data held by banks to unlock new possibilities. By gaining deep insights into users' financial behaviours, businesses can seize exciting opportunities and become an integral part of their customers' everyday journeys.
Fetch users' real-time and history of transactions from all of their bank and financial institution accounts directly via the Account Aggregator framework.
Sanitise raw and confusing bank transaction strings to get clear and easy-to-read transactions that make sense to identify, organise and act on.
With our rule-based system and ML capabilities, which have been trained on millions of transactions, identify categories and sub-categories granularly, as well as merchants, methods, and modes for each transaction with up to 95% accuracy.
Add context and metadata to identify the when, where and how of every transaction and enhance geolocation, category and merchant information including its name, logo and website.
Extract deep behavioural attributes of your consumers to gain insight into recurring expenses, subscriptions, deposits, regular inflows, risk indicators or any unique user behaviour data.
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Whether your own data pipeline or via FegoConnect, TrACE extracts deep behavioural attributes from banking transaction strings by constantly enriching and identifying categories & sub-categories granularly.
Whether your own data pipeline or via FegoConnect, TrACE extracts deep behavioural attributes from banking transaction strings by constantly enriching and identifying categories & sub-categories granularly.
Whether your own data pipeline or via FegoConnect, TrACE extracts deep behavioural attributes from banking transaction strings by constantly enriching and identifying categories & sub-categories granularly.
Whether your own data pipeline or via FegoConnect, TrACE extracts deep behavioural attributes from banking transaction strings by constantly enriching and identifying categories & sub-categories granularly.
Whether your own data pipeline or via FegoConnect, TrACE extracts deep behavioural attributes from banking transaction strings by constantly enriching and identifying categories & sub-categories granularly.
Bank narration strings are notoriously hard to understand strewn with various alphanumeric characters, truncated names of merchants and various delimiters. To add to it every financial institution has its own syntax making it even messier. But hidden in these strings, are secrets to people's financial behaviour, akin to DNA containing all affirmative financial acts that define a person.
Transaction data is deeply entrenched in multiple processes as it helps Financial Institutions understand and define the users they are working with. Crucially, Banks sit on rich personal and financial data that captures all major milestones in users' lives. Understanding their past and current behaviour empowers businesses to recommend exciting opportunities and be a part of their users' daily lives.
Innovate faster, deliver personalised financial experiences & deepen customer engagement with enriched transaction data
Widen offerings and go-to-market faster with nimble products and seamless financial insights
Introduce finance to your users and be at the cutting edge of finnovation by building on top of Fego
