Author: Will Towning, Central Banks and Academia Programme Manager
What is intraday liquidity?
Intraday liquidity is the funds financial institutions can access during the business day in order to make payments in real time.
Acute liquidity stresses during the Global Financial Crisis (GFC) highlighted the need for banks to improve intraday liquidity management. Since then, advances in technology, regulation and monetary frameworks have intensified banks’ focus on the sources and uses of liquidity that can affect the bottom line.
Exploiting data-driven solutions can reduce liquidity usage and improve payments functioning.
What is Intraday Liquidity Optimisation?
Intraday liquidity optimisation (ILO) is an advanced analytics and simulation approach to help financial institutions minimise liquidity usage and payment bottlenecks. The solution can be applied to any collection of payments within an organisation spanning internal and external accounts, geographic locations, and currencies.
There are typically two ILO approaches a bank can take, depending on their needs and the maturity of their current operational systems: ex-post simulation or near real-time simulation.
Ex-post simulation approaches create a replica network of a bank’s payments operations using historical payments data and incorporate its operational policies. The replica is then used to measure, simulate, optimise and stress test intraday liquidity usage:
Step 1: Measure current liquidity usage and delays at the network and account level.
Step 2: Simulate historical periods to identify choke points, delays and inefficiencies in the payment network.
Step 3: Optimise operations by running multiple further simulations, introducing a range of liquidity saving mechanisms. Identify policies and strategies that minimise liquidity usage and payment bottlenecks.
Step 4: Stress test the payments network against tailor-made disruptions. For example, a bank can identify any number of accounts and designate them as a ‘stricken’ entity to evaluate the resulting payment dynamics and mitigate risks.
In contrast, near real-time simulation approaches integrate with a bank’s existing cash management and treasury systems. As a result, it is most applicable for financial institutions with more mature operational systems. The approach works dynamically to measure, optimise, forecast and stress test a bank’s intraday liquidity needs.
Step 1: Measure near real-time liquidity usage, flows and delays at the network and account level.
Step 2: Optimise operations by deploying machine learning algorithms to explore various settlement, trading and payments techniques which can reduce liquidity usage.
Step 3: Forecast upcoming liquidity requirements using past data or data from front office systems. Practitioners can also project probabilistic requirements by integrating macro factors such as seasonality or foreign exchange rates.
Step 4: Stress test payments network in near real-time against tailor-made disruptions.
Why does liquidity optimisation matter?
Intense regulatory and supervisory expectations under the Basel Committee on Banking Supervision’s 248 standards, as well as the low interest rate environment, following the GFC have impacted financial institutions’ ability to deliver strong returns on equity. As a result, intraday liquidity today represents a major portion of bank reserves, and funding costs are increasingly important for the bottom line.
Financial institutions today also operate under complex legal entity structures, settled globally through a large number of nostros and agents, with a multitude of accounts, across various currencies.
To effectively manage payments operations, treasury departments must increasingly balance all these challenges with faster settlement speeds.
Network analytics and simulation provides treasures with the visibility and foresight over the flows of internal and external funds. Applying these tools helps identify key inefficiencies, payment delays and sources of risk, highlighting previously unseen opportunities for greater operational efficiency and resiliency.
With these capabilities, treasurers can make substantial liquidity savings and protect their institutions from potential funding pressures in the future.
Tailoring your ILO needs
Financial institutions’ operational functions and objectives differ significantly and, as a result, their liquidity problems and optimisation goals will also vary.
One financial institution may operate several legal entities, and associated currencies, across the globe, with both group and country-specific treasury functions. This bank may need a network approach to understand the best way to manage funds internally.
Another financial institution, which faces payments delays when interacting with the payment system, may wish to identify methods to improve their external flows and reduce liquidity usage or bottlenecks associated with these flows.
While a third case may be a financial institution concerned with settlement times across a network of nostro bank accounts. Network analytics, simulations and visualisations could identify liquidity pressures between a subsidiary and a nostro account and offer solutions to mitigate such pressure.
By building a digital replica of a bank’s payments network, or integrating directly with its cash management systems, ILO approaches offer complete flexibility to solve challenges unique to each institution.