Highly accurate, cost-effective categorisation
Machine learning that has been trained by real people and businesses for automated underwriting decisions, investment affordability assessment and more.
Categorise data simply and automatically
Benefit from community-based knowledge derived from years of machine-learning, and enhanced with your own users data. Machine-learning learns from customer input, to continually refine, improve and clean transactions to the benefit of all users.
Unprecedented accuracy
Allowing you to automate underwriting decisions, investment affordability assessment and much more.
Categorisation accuracy
Split transactions and enable further refinement for correct recording. Even cash withdrawn from ATMs can be split into multiple transactions to be accurately categorised and recorded.
Save money and time
Leave behind ineffective, inefficient and costly modes of categorisation, where businesses pay teams of people to review and improve transaction quality.
Data enrichment and geo-location
Transaction categorisation is just the start of our Data Enrichment, which includes recurring transaction prediction, balance prediction and counterparty detection. This area can easily be extended to include the geo-location (provided by the CMA9 FAPI specification for Open Banking), so that the transaction categorisation can then be location-enabled.
Constant improvement
The machine-learning engine is constantly updating and improving as more and more data becomes available.
Transfers
Transfers between accounts are well recorded and treated differently to actual transactions, ensuring accuracy is not affected through the poor handling of transfers between various bank accounts.