Banking & Financial

Financial institutions sharing fraud and cybercrime intelligence face a hard problem: the data grows faster than the systems built to handle it. One member owned crime prevention body found itself managing millions of records tied to suspicious transactions, using a collaboration tool that was never designed for that volume and every added data type slowed the platform further. 

Rather than rip out the front-end system that banks and analysts already relied on, we built a new data layer underneath it. The existing collaboration tool stayed in place for day-to-day work, while a dedicated analytics engine took over the heavy lifting of processing and connecting fraud data behind the scenes. The result: an organisation now managing more than eleven distinct, highly complex data types drawn from major banks, with the platform running fast enough to keep pace with a caseload that keeps climbing. 

Ongoing maintenance dropped to a level their own team could handle without added headcount. For an industry where slow data access means fraud goes undetected a day longer, that speed and the eventual ability to cross reference data no single institution could see alone, is the whole point.