The power of analytics in real-time liquidity management

real-time liquidity management
Pete McIntyre, the liquidity expert

Written by Pete McIntyre

February 5, 2024

Data helps treasury improve. A robust data infrastructure is critical for strategic planning and also in real-time liquidity management. Having the ability to look back, look forward and look now is crucial for a bank to assess its positions and potential vulnerabilities. Without real-time data you can’t do any of this! 

The evolution of analytics

Historically, banks directed their data efforts towards compliance and meeting institutional reporting obligations. Today, the focus has shifted towards leveraging analytics as a profit driver. 

Advanced analytics techniques have evolved to play a pivotal role in long-term strategic planning. This evolution allows banks to streamline payment operations and allocate liquidity resources effectively based on predictive modeling. By focusing on past behaviors to predict inflows and outflows, data analytics goes beyond compliance, creating strategic advantages for financial institutions.

What is predictive analytics

Predictive analytics, rooted in machine learning algorithms and statistical modeling, is a cornerstone of contemporary banking operations. It enables the analysis of current and historical data to forecast future states, including crucial aspects like liquidity positions. The application of data science offers liquidity management professionals a strategic edge, empowering them to foresee potential gaps or excesses and adjust strategies proactively amidst market fluctuations.

Understanding liquidity flows

Liquidity-based data analytics involves examining the inflows and outflows of cash, the maturity profiles of assets and liabilities, and the potential impact of market conditions on liquidity. It encompasses a range of activities, from monitoring real-time transactions to forecasting future cash requirements, and from stress testing under various scenarios to optimising the balance between liquid assets and liabilities.

Banks utilise liquidity-based data analytics to ensure they maintain adequate levels of liquid assets to safeguard against any potential liquidity crunches that could arise from market volatility, operational issues, or sudden spikes in customer withdrawal demands. This form of analytics is also instrumental in identifying the cost of liquidity, such as the opportunity cost of holding high levels of cash or the expense of obtaining funds from external sources during a liquidity shortfall.

A more complete understanding of liquidity positions can also enable banks to streamline processes, minimise operational costs, and maximise profit-driving opportunities. One clear example of this is in the separation between operational and non-operational money. The better a bank is able to understand and predict inflows and outflows, the more efficiently it can deploy capital – for example, by making appropriate lending decisions while retaining enough cash on hand to service obligations and meet regulatory requirements.

The larger and more efficiently targeted the data being collected, the more complete the picture available to the institution. The first step towards driving efficiencies is to build knowledge of the current position, meaning that banks need visibility and understanding of their current liquidity flows. By evaluating the nature of each of the assets within their portfolio, along with their liabilities, institutions can begin to predict future patterns and plan for changes. On a big-picture basis those might be as significant as a change in the base rate; on a smaller but no less important scale, they might be a change in the stability of an individual asset class.

Technology drives change

Advanced analytics techniques are enabled by developments in digital technology. These developments include reductions in the cost of computer functions, rapid increases in computing power itself, and the accessibility of large-scale data storage.

Data lakes, cloud-based (or off-premises) storage, and the shift towards ‘composable’ tech stacks as enabled by the adoption of the API model, have all played significant roles in the development of modern data analytics and their accessibility. Today, cutting-edge analytics capabilities are available to banks and other institutions of virtually every size through platforms such as Planixs’s Realiti Control.

Realiti Control, and its partner Realiti Insights, have been adopted by top-tier banks that now use the real-time liquidity data provided to power and inform their treasury decisions. The adoption of a real-time liquidity management platform allows institutions to unlock more of the value in the assets they already hold.

“Realiti Insights offers intraday liquidity managers the ability to review the timeline of what actually happened vs. what was expected and drill down into the root causes. Internally, were things booked late? Externally, did counterparts pay late? That insight can be used to drive a change of behaviour.” Olaf Ransome, Liquidity Futurologist, Planixs

Through real-time cash visibility, institutions can optimise their liquidity management, cut operational costs, ensure regulatory compliance, and streamline operations – all without the cost or stress of building a solution in-house.

Ready to meet Realiti? Talk to us today.

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