The digital-first bank’s guide to AI in 2026
Financial institutions are pouring $97 billion into AI by 2027, but here's what nobody tells you: 95% of AI projects fail to deliver on their promises. The reason? Bad data. Not bad algorithms, not insufficient budget, just terrible, siloed, inaccessible data that no AI can work with.
Before you buy another chatbot platform or fraud detection system, ask yourself: can you actually access your own banking data in real-time? Can your data scientists query your core without crashing production systems? Do you have a single source of truth, or are you duct-taping together CSV exports and hoping for the best?
This guide shows you how to build the foundation that makes AI actually work. We cover everything from credit scoring with alternative data to operational automation that cuts costs by 40%. But most importantly, we show you why your data layer matters more than any algorithm and how to fix it before you waste money on AI that goes nowhere.