In Formula 1, in-race tactics are constantly evolving. Teams rely on minute-to-minute information to decide on the best time to change tyres, adjust a vehicle’s set-up and refuel. Often, those plans also have to adapt on the fly when the weather changes or an incident happens on the track.

Much of the same can be applied to the world of intra-day liquidity management. It all starts by looking to cash ladders and forecasts for an overview of expected conditions, with dashboards steering a team throughout the day. More advanced drivers might also incorporate behavioural analysis and sentiment to guide them should conditions change.

So far in this series, I’ve explored how organisations can learn from F1 when it comes to building a firmer risk framework. In this blog, I take a deeper look at how organisations can apply similar thinking to manage their intra-day liquidity needs more effectively.

1. How clear is your cockpit?

As a driving force of decision-making, your dashboard should be clear and concise. It should provide easy visibility of liquidity flows, and allow the team to spot early signs of difficulty and assess if there are tricky conditions that need to be managed. The dashboard should be defined in the context of the organisation to focus on material risks and reflect the liquidity strategy. To make it meaningful, it should provide an aggregated view of management and regulatory needs. 

Equally, you should be able to drill through the aggregation to understand the underlying drivers and source of the gross flows. This helps you understand if there are actions that you need to take for a particular counterparty or beneficiary. Those actions will differ whether you are acting as a principal or agent. You will need to understand which entities, currencies and business lines are impacted. Ideally, you will understand any wider implications and sensitivities that also need to be managed and have stress-tested them to provide a playbook of actions you could take. Just like in F1, preparation and testing are everything.

But, of course, if warnings start flashing in the cockpit and no one trusts the underlying data, then the driver doesn’t have a clear course of action. This leads to confusion and poor decision-making.

2. Is your team giving you all the data you need?

When conditions are clear, you can easily focus on daily tasks. But if they deteriorate, you need to understand how to drive in tougher conditions. That’s where clear, good-quality data becomes essential.

To understand what is driving a problem, you need to trace data back to the source at the gross level. Is this an internal issue, such as failed trades, or are external factors at play, like a counterparty, client or wider market conditions, sentiment or behaviours? The quicker you can identify the source of the problem, the quicker you can act and decide, “Do I stay on track or make a pit stop, tighten up my handling, refuel and get back out there?”

The sticking point is that liquidity management is rarely as easy as that. Transactions are captured through multiple mechanisms and systems; many are for future settlement, while some are for the same day and can vary from team to team. These transactions can be internal or external, made on behalf of the institution or as part of client activity. As a result, they can be subject to compression, netting or aggregation either externally with a counterparty or internally to a consolidated entity or back-to-back trades.

Netting agreements tend to be complex and, in some cases, manual; they can be facilitated by clearing counterparts and custodians, and a bilateral counterpart. This may help to manage settlement risk but creates challenges when trying to trace the underlying data back to the gross flow when working to resolve an issue or manage a stress situation.

How, then, do you manage all of this complexity? As is the case in F1, banks can make use of a variety of cloud-based data management and analytics tools, which support both structured and unstructured data. Increasingly, there is also the possibility to overlay the data model with generative AI for data cleansing, deeper learning and more sophisticated analysis. While still in its infancy, GenAI holds great potential for recognising sensitivities and interconnected patterns – providing results that are data-driven without human bias to support smarter decision-making.

3. How responsive is your throttle? What other leavers can you pull, and when?

Monitoring current and predictive positions is a good starting point – but you also need to be able to throttle up or slow down as the situation requires. It means that information exchange between trading, settlement, liquidity management systems, payments applications, gateways and reference data platforms must be rapid and responsive. That way, if something unexpected comes up, you get the earliest warning on your dashboard.

Integral to this framework is the active management of collateral. In addition to tracking where cash is moving, firms should know the amount and availability of unencumbered assets and have the mechanisms to mobilise them promptly. This means assessing the eligibility of asset classes held for Central Banks and the acceptability of assets to counterparties and investors in secured funding markets.

The faster you can mobilise your cash and collateral and manoeuvre it to where it is needed, the better your position will be compared to your peers. How you respond, however, depends on your planning, experience and governance process. Historical data analysis is key to understanding how intra-day liquidity moves against various internal, micro and macro factors. By applying business intelligence to this data, you can start to understand sensitivity and patterns, using this information to drive limits and early warning indicators. And shifting into higher gear with real-time analyses allows you to create dynamic limit structures that adjust to market and business conditions on the day.

Experience and knowledge should be documented in playbooks that set out how to act in various plausible scenarios. Stress testing plays into this by calibrating the potential impacts and actions to be taken. The nature of the decisions and governance that supports them should be clear and transparent to allow management to be confident in the experience and knowledge of the team to manage any conditions and consult as required.

Is it time to take another look at your race strategy?

The Bank of England, Federal Reserve Board, Basel Committee of Banking Supervision and others have written extensively on the turmoil experienced in the first half of 2023, and the inability of institutions to maintain sufficient stable funding given the scale and speed of outflows experienced.

There were several factors preventing liquidity from not being deployed in the manner envisaged, including the calibration of existing requirements and operational constraints. They will only create more bumps and financial debris, at the same time as customer demands for “real-time” accessibility continue to increase, supported by market infrastructure changes as atomic settlement evolves.

Even as organisations must look ahead to steer a safe course through these conditions, they can’t afford to take their eyes off the track right in front of them. Now more than ever, they need to ensure that their strategy is robust enough to weather day-to-day demands and flexible enough to cope with unexpected bumps in the road ahead.  

At Baringa we are working with our clients to shape the future of intra-day liquidity management – from creating proof of value, tracing cash and collateral, wargaming and enhancing stress testing to building analytical capabilities and taking them further with tools like GenAI. If you think your organisation could benefit from some F1 thinking, reach out and we’ll be ready to support you.

Find our more on how firms can apply Formula 1 thinking to their risk management strategy.

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