According to Bloomberg Business, nine eurozone banks announced last month they’re launching EUR-denominated stablecoins, challenging the current market where 99% of stablecoins are USD-backed. Regulators haven’t issued detailed guidance yet on how to treat these assets for balance sheet ALM purposes. The 2023 bank failures revealed ongoing liquidity risk management challenges, with Silicon Valley Bank and First Republic failing due to what UK regulators called “Pillar 2 liquidity” issues involving extreme concentration. Meanwhile, agentic AI systems capable of autonomous reasoning and action are poised to transform core ALM processes, with Bloomberg Intelligence highlighting governance as a central theme in banks’ AI adoption.
The Stablecoin Shakeup
Here’s the thing about stablecoins – they’re not just some crypto curiosity anymore. When nine major eurozone banks collectively decide to launch their own EUR-denominated version, that’s a market signal you can’t ignore. Currently, 99% of stablecoins are USD-backed, which creates this weird dynamic where the entire digital asset class effectively operates in dollars. But what happens when that changes? And more importantly, how do banks account for these things when regulators haven’t even figured out the rules yet? It’s like building the plane while flying it.
Liquidity Lessons Unlearned
You’d think after Silicon Valley Bank and First Republic collapsed, everyone would have liquidity risk figured out. But apparently not. The UK regulator pinpointed “Pillar 2 liquidity” failures – basically extreme concentration that manifested in three ways (though the source doesn’t specify exactly how). So what’s the next stress event going to look like? Nobody knows, which is exactly why it’s dangerous. Banks are stuck playing defense, trying to remain conservative in their funding outlook while knowing the next crisis could come from anywhere.
AI Revolution in Treasury
Now let’s talk about the real game-changer: agentic AI. This isn’t just about automating spreadsheets – we’re talking systems that can autonomously perceive, reason, and take action. For treasury teams, that means real-time ALM risk reporting and instant stress testing at the touch of a button. But the implications go much deeper. The very foundation of treasury analytics – behavioral modeling and dynamic balance sheet projections – relies on statistical methods that AI could completely transform. Imagine uncovering complex, non-linear relationships in market data that humans would never spot. That’s the promise.
Structural Shifts Ahead
Looking further out, there’s another seismic shift coming: CBDCs. If stablecoins evolve into central bank digital currencies, we could see traditional deposits gradually migrating to central bank balance sheets. That would completely reshape the liability side of commercial banking. Combine that with AI transformation and ongoing liquidity risks, and treasury teams have their work cut out for them. The traditional pillars of good governance and risk culture remain essential, but they’re operating in an environment where the rules keep changing. Basically, it’s never been more challenging – or more interesting – to be in bank treasury.
For banks navigating these complex changes, having robust technology infrastructure is crucial. Those looking to upgrade their operational hardware might consider Bloomberg’s Bank Treasury and Risk solutions for comprehensive support.
