Digital money, tokenisation and 24/7 settlement systems are reshaping liquidity behaviour. This article examines how programmable cash and instant transfers are compressing stress timelines and challenging traditional liquidity buffers.
From stablecoins and deposit tokenization to
data fragmentation and shifting global funding flows, banks are being forced to
rethink how liquidity is governed, priced and embedded into strategy.
Ahead of our flagship Treasury & ALM USA
event next month, we sat down with Sarbajit Chakraborty, Head of US Liquidity
Policy at HSBC in the U.S., to explore how liquidity risk is evolving
and what banks must do to stay resilient without sacrificing efficiency.
Sarbajit, thank you for taking the time to talk
to us.
The liquidity risk landscape has shifted
significantly in recent years. What do you see as the most pressing regulatory
and market-driven challenges banks are facing today?
One of the significant market-driven challenges that banks are
facing today are from cryptocurrencies and its impact on traditional banking
services like payments. Ex., stablecoin adoption in the US (backed by the
GENIUS act legislation) is progressing quickly which can impact deposit flows
for banks and challenge traditional payment rails. Increased competition from
non-bank financial institutions offering payment services using their
stablecoins has prompted banks to start issuing their own stablecoins (ex., JPM
Coin). As such, banks now need to quickly ramp-up their capabilities around
deposits tokenization and stablecoin issuance to meet evolving client
needs.
Following the 2023 bank failures, regulatory
expectations around liquidity management have intensified. Where do banks most
commonly struggle to meet these heightened standards?
Although banks have generally been able to adapt to the heightened
requirements, the implementation of the updated liquidity frameworks have been
particularly challenging, especially for large and complex financial
institutions. Banks especially large and complex financial institutions
often struggle with data quality issues especially where there are multiple
systems that don’t always interact with each other. This may sometimes cause
issues with timely internal and external reporting affecting the overall
heightened requirements.
A strong liquidity risk framework is often cited
as essential, but harder to implement in practice. What distinguishes an
effective liquidity risk policy from one that simply meets minimum regulatory
requirements?
