The author says Asia’s instant payments outrun lagged stats and old forecast models. Private desks already trade on millisecond sentiment; central banks need AI-backed suptech. Example: Bank Indonesia’s QRIS topped 5.22 billion monthly transactions by mid-2026 ( 95.1% y/y) with Thailand, Malaysia, Singapore, and Japan links. Governor Perry Warjiyo: AI can sharpen analysis if data and staff come first, not trophy software.

Risks named: herding if many banks share the same vendor models and dump together; model poisoning of pricing/liquidity inputs; lock-in to Western clouds and tools, with sovereignty worries. Systems must stay interpretable.

Eleven EMEAP governors in Singapore (23–24 July 2026) agreed to align AI supervision—vendors, cross-border spillover, fraud. Indonesia chairs in 2027; the author wants a regional sandbox to stress-test models against panics, bad data, and capital flight. Goal: govern AI rather than race to adopt it, and offer the Global South a template.

Something profound is happening in how money moves across Asia. Shoppers and small merchants now clear millions of transactions on the spot—tapping a phone at a street stall or wiring money across borders in seconds. Behind those routine phone taps lies a complex web of high-frequency clearing networks and automated liquidity tools running non-stop to keep regional markets afloat.

Yet central banks in Emerging Asia face a tough environment. Geopolitical fragmentation, sustained high interest rates globally, and erratic capital movements have exposed the limits of traditional forecasting models that rely on lagged economic statistics. Monetary authorities are naturally turning to artificial intelligence to fill these gaps. But adopting AI without a shared governance framework risks introducing fresh vulnerabilities into the very financial rails meant to protect regional........

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