Banks are investing in AI, but much of the data that makes banking AI valuable, including balances, transactions, customer history and operational context, remains locked in mainframe formats that modern analytics and AI tools cannot easily read. Accessing it often requires slow, brittle extraction and transformation pipelines.
60% of Tier-1 banks
identified existing/legacy data warehouse architectures as a barrier to improving decision intelligence.
Celent’s 2025 Decision Intelligence and Data Survey
This data-access gap can limit investigation, reconciliation and AI-driven insight. Teams may need to extract, decode, transform and stage
