24/7 Payments, Zero Leeway: How Banks Are Overcoming the Liquidity Challenge

Until now, liquidity management has been relatively predictable. Banks worked with predictable booking windows, batch processing and fixed monitoring times. Weekends and public holidays were reliable off-peak times. With the introduction of instant payments, this predictability is a thing of the past: payments are made around the clock, regardless of regular working hours.

Many institutions are still underestimating the urgency of adapting. However, the potential use of instant payments will increase, especially among corporate customers who value real-time payments. With this change, traditional SEPA payments and thus also the controllability of traditional control tools are declining.

The transition to instant payments is forcing treasury departments to perform a balancing act. On the one hand, ensuring continuous payments would be possible by maintaining unnecessarily high liquidity reserves, but this reduces profitability. On the other hand, banks risk payment rejections due to a lack of monitoring and insufficient liquidity – with potential damage to their reputation and customer confidence.

A purely reactive approach will no longer suffice in the future. Real-time monitoring, accurate forecasts and flexible, data-based control models are required.

The ECB has formulated clear expectations:

• Identification of time-critical payments: Liabilities and transactions with specific deadlines must be identified and prioritised. 
• Real-time monitoring: Payment flows should be aggregated and analysed in detail.
• Liquidity forecasts: Models for predicting liquidity requirements based on historical data are essential. In particular, the largest cumulative negative net position (LNNCP) is crucial, as it describes the highest expected liquidity requirements for a defined period.

Other requirements include short-term forecasts for time windows of 30 and 120 minutes, up to day-long forecasts. This requires high-performance technologies and algorithms. Modern methods such as predictive analytics and machine learning are becoming central to treasury management.

A well-designed forecasting model allows for flexible liquidity management, minimises capital commitment and prevents bottlenecks. Banks that invest early on secure their competitiveness and reduce costs in the long term. At the latest, when instant payments become the ‘new normal’, an intelligent forecasting model will become indispensable – because by then, there will be no room for improvement.

Real-time payments further complicate liquidity management – rigid, traditional approaches are not sustainable. Treasury departments must be data-driven, flexible, and adaptive to remain capable of acting and cost-optimised at all times. Therefore, those who do not develop forward-looking control systems now risk losses – both financial and reputational.