AI in banking hits $97B by 2027—but data silos threaten progress

Artificial intelligence is transforming banking, with spending in the sector expected to hit US$97 billion by 2027. Many institutions now rely on AI to detect fraud, assess credit risks and improve customer service. Yet the technology's success depends heavily on one key factor: strong data infrastructure.

Fraud detection remains the most common AI application in banking, with 90% of institutions adopting it. These systems achieve accuracy rates of 90% to 99%, far outperforming older rule-based methods. Beyond security, AI also enhances lending decisions by improving default prediction accuracy by 15% to 25%. In emerging markets, this has led to loan loss reductions of up to 30%.

Despite these gains, banks face a major hurdle: fragmented data. Core banking platforms and operational systems often store information in silos, making it difficult to deploy AI at scale. Without unified, production-grade data, even advanced models struggle to deliver results. Customer service has also benefited from AI, with chatbots and automation handling routine inquiries. Personalisation tools analyse spending habits and product usage to tailor recommendations. However, transparency remains critical—banks must ensure AI-driven decisions, whether in lending or fraud detection, can be explained and justified. While AI adoption grows, its impact varies by region. In markets with limited credit data, concrete examples of successful AI credit scoring remain scarce. No specific figures exist for how many institutions use proven AI solutions in these areas, and few pilot projects have been documented in detail.

The future of AI in banking will likely hinge on data readiness. Institutions that consolidate their systems and improve data access could gain a significant edge. With spending set to rise, the focus will shift from simply adopting AI to making it work effectively across all operations.