Data Gaps Slowing AI Adoption in Banking

The core issue isn't a lack of data, but its fragmentation across decades of siloed systems. More than half of banks report that this prevents real-time decision-making, a critical capability for AI-driven applications like fraud detection and risk analytics. This reliance on structured, historical data processed in batches severely constrains the scalability of modern AI initiatives. Legacy infrastructure creates a massive financial drain, with some banks spending 70-75% of their IT budgets just on maintaining these outdated systems. This leaves minimal resources for innovation. The total cost of ownership for these legacy systems is often underestimated by 70-80%, with the actual cost being up to 3.4 times higher than budgeted when indirect costs like inefficiencies and lost opportunities are factored in. This "stack gap"—the chasm between AI's demands and existing infrastructure—could represent a hidden cost of over $100 billion globally for the banking industry. The complexity of integrating modern AI with hundreds of legacy systems, where each connection can cost between $100,000 and $500,000, is a primary driver of this expense. This technical debt is a major reason why up to 80% of AI projects fail to move past the pilot stage. Despite these hurdles, the push for AI is accelerating, largely driven by the fight against financial crime. AI-driven fraud, using deepfakes and synthetic identities, now accounts for 42.5% of all fraud attempts in the financial sector. In response, 90% of financial institutions are now using AI to combat these emerging threats and speed up investigations. Beyond risk management, AI is being deployed to enhance customer experience and operational efficiency. Bank of America's AI assistant, "Erica," handled 676 million interactions in 2024, while NatWest's "Cora" managed 11.2 million customer conversations. JPMorgan Chase's COiN platform uses AI to analyze complex legal documents, reviewing 12,000 commercial credit agreements in seconds—a task that would have taken lawyers over 360,000 hours. Successfully scaling AI requires a fundamental shift from viewing it as a series of projects to treating it as core infrastructure. This involves modernizing data strategies to support real-time data streams and unifying disparate data sources. The competitive advantage in the near future will be determined not by the sophistication of AI models, but by the speed and quality of the data that fuels them.