Report: Data Gaps Stalling AI in Banking

The core technical hurdle is that most legacy banking systems were built for stability, processing transactions in batches overnight. Modern AI fraud detection models, however, require real-time data streaming to spot and block suspicious activity in milliseconds, before funds are lost—a fundamental architectural mismatch. Data scientists at financial institutions can spend up to 70-80% of their time simply trying to find and clean the right data, a consequence of decades of data being stored in disconnected silos across different business units. A recent survey found 97% of financial services organizations report that these data silos are a direct barrier to developing and deploying effective AI models. This challenge mirrors the evolution of recommendation engines at companies like Netflix, which combine offline batch processing (for training complex models) with online, real-time computation to react instantly to user interactions. Banks need a similar hybrid data infrastructure to personalize services, but most are still stuck in the offline, batch-only world. To bridge this gap, the industry is turning to MLOps (Machine Learning Operations), a set of practices that automates the entire ML lifecycle, from data preparation to model deployment and monitoring. This framework is critical in a highly regulated environment to ensure model governance, manage risk, and connect modern AI workflows to older, legacy systems. The inability to process unstructured and real-time data also hinders the deployment of more advanced generative AI. While LLMs can be used to summarize compliance documents or simulate risk scenarios, their effectiveness is capped by the quality and accessibility of underlying enterprise data. Ultimately, solving this data problem unlocks significant value. Mature, AI-driven personalization can boost revenue by up to 25% and conversions by 10-15% by dynamically tailoring products and advice to individual customers in real-time.