Why AI Pilots Fail and How Banks Can Fix It

Banks in Singapore have poured millions into AI pilots over the past two years — proofs of concept for fraud detection, customer service chatbots, credit scoring models — yet most of these projects never make it to production. The common explanation is that the models aren't good enough, or there aren't enough data scientists. But a closer look suggests the bottleneck isn't the AI itself; it's the core banking infrastructure underneath.

This is the central argument of a new analysis from Oradian, a core banking platform provider, published by Fintech News Singapore on July 30. The piece argues that traditional core banking platforms were built for human operators working through screens, not for AI agents that need to read live data, call APIs, trigger workflows, and make decisions under governance. Without governed data access, complete API coverage, and auditable execution controls, AI remains an isolated experiment rather than becoming part of everyday banking operations.

The structural gap matters because Singapore's banking sector — home to DBS, OCBC, and UOB, plus regional and global banks with significant operations here — is one of the most digitally mature in Asia. If Singapore's banks can't bridge the gap between AI pilots and production deployment, the problem isn't Singapore-specific technology capability. It's an architectural challenge shared by financial institutions globally: legacy core systems designed decades before AI was on anyone's roadmap.

Oradian's prescription is what it calls an "AI-native" core banking platform — one that provides an API-first architecture where every banking capability is securely exposed, open data access that separates analytics from production workloads, native AI capabilities for product development and operations, and governed execution with version control and full auditability. The argument is that competitive advantage in the AI era won't come from having the best model, but from how fast an institution can turn ideas into products and adapt to changing conditions.

Why it matters for Singapore: Singapore's position as a financial hub means its banks are among the first to confront this AI-versus-legacy-infrastructure tension at scale. The three local banking groups — DBS, OCBC, and UOB — are all actively investing in agentic AI, generative AI assistants, and AI-driven operations. But if the underlying core platforms can't support production AI workloads, those investments risk getting stuck at the pilot stage. The architectural choices Singapore's financial institutions make today — whether to retrofit legacy cores or adopt AI-native platforms — will determine whether the country's banking sector leads or lags in the AI era. For the broader SG AI ecosystem, the lesson extends beyond banking: infrastructure readiness, not model capability, is increasingly the rate-limiting factor for AI deployment across regulated industries.