Data Gaps Hindering AI Adoption in Banking
Legacy data environments and a lack of real-time data are hindering the banking industry's ability to scale AI initiatives, according to a new report. While banks are eager to use AI for fraud detection and personalization, many are hitting structural limitations that prevent effective implementation.
Many of the world's largest banks are still powered by core systems developed between the 1970s and 1990s. These monolithic platforms, often built on languages like COBOL, were designed for stability in transaction processing, not the real-time data analytics required by modern AI. This legacy infrastructure creates significant data silos, a problem cited by 59% of bankers as a major business challenge. Nearly one in five banks still operates with a siloed data environment, meaning crucial information is fragmented across incompatible systems, making it inaccessible for AI model training. The issue is compounded by a reliance on batch processing, where data is updated periodically rather than in real-time. This fundamentally conflicts with the needs of AI in areas like fraud detection, which depends on analyzing transaction data milliseconds after it's created to be effective. Despite these hurdles, the financial incentive is massive. Global spending on AI in banking is projected to surge to $450 billion by 2027. Generative AI alone could add between $200 billion and $340 billion in annual value to the sector, representing a powerful motivator to solve the underlying data issues. In fraud detection, the impact of real-time AI is clear. For every dollar lost to fraud, banks incur nearly three dollars in associated costs. AI-powered systems have been shown to increase fraud detection accuracy by 20% and reduce false positives by 30% compared to older, rule-based methods. The challenge isn't purely technical; it'
