Yet most struggle amidst complexities of the data itself, regulations, and more, to get AI initiatives off the ground. But they don’t have to. Ultimately, AI for banks means turning data from the cost center it is today in to a revenue stream – a source of efficiency and a wealth of information that can be used to provide fundamental value to the business. This white paper aims to put financial institutions on the path to realising that potential.
Data has always been the foundation of the banking industry. What has changed in recent years is the amount of data available and the speed at which it is processed as well as the need to quickly respond to market changes. New technology gives banks the power to collect, store, and analyse exponentially more information than was imaginable not too long ago. In the wake of Fintech, banks already know that to succeed in today’s ecosystem, they must use this wealth data at a massive scale to continuously innovate.
Get started now, because waiting a few more years to dive in will mean pushing the timeline of the Enterprise AI journey even further, while competition from other more agile companies (whether fintech, GAFA – Google, Apple, Facebook, Amazon – or traditional players) moves in