How Banks are Leveraging Structured and Unstructured Data for AI

Banks are increasingly leveraging AI to harness both structured and unstructured data, but face significant challenges in doing so. While initial successes were achieved with unstructured data applications, such as document summarization and conversational AI tools, many financial institutions struggle to effectively utilize their vast stores of structured transactional data due to its complexity and the presence of data silos. The industry is now evolving towards agentic AI systems that can intelligently route queries across different data types in real time. To support this shift, banks are building robust data foundations that integrate structured and unstructured data, requiring advanced infrastructure capable of handling diverse workloads and federated data access. This progression highlights the need for modern data architectures and governance frameworks to facilitate seamless data accessibility and integration, ultimately providing a competitive edge in customer insights and business intelligence.