Liquidity Lowdown: Developments of AI in cash management

For cash managers operating in a data-heavy, time-sensitive environment, artificial intelligence (AI) is presenting significant opportunities. Advances in AI are demonstrating their value across a wide range of financial processes, including cash management. While the core principles of cash management — security, diversification, and risk management — remain unchanged, AI is increasingly being used as a tool to enhance efficiency, improve data analysis, and support decision-making.

AI has an incredible ability to process and analyse large volumes of data at a speed well beyond human capability. Cash managers monitor a wide range of data inputs, including interest rate expectations, credit spreads, issuer fundamentals and news flow. Traditionally, much of this work has been manual and time-consuming, relying on spreadsheets, disparate data sources, and human interpretation. AI has the capacity to collate data from multiple sources, identify patterns, and present insights in a structured format. This can enhance the decision-making framework, allowing managers to make better informed and more timely investment decisions.

By automating some of the more routine aspects of the role, AI enables cash managers to dedicate greater attention to portfolio construction and identifying opportunities to add value into their funds.

AI also offers significant advantages in handling repetitive and process-driven tasks. Daily activities such as data entry or report generation can be automated, freeing up time for cash managers to focus their attention elsewhere. This efficiency gain is particularly relevant in money market fund management, where operational workflows are not only time consuming, but time sensitive. By automating some of the more routine aspects of the role, AI enables cash managers to dedicate greater attention to portfolio construction and identifying opportunities to add value into their funds.

Another area where AI shows promise is in supporting credit analysis. Assessing the creditworthiness of issuers is a fundamental part of cash management, requiring analysis of financial statements and ratios, market indicators, and qualitative factors. AI can assist in building tools that compile and organise relevant information on issuers, providing a consistent framework for evaluation. For instance, AI can aggregate financial metrics, track changes in credit ratings, and monitor news flow, presenting this information in a cohesive and consistent format. While this does not replace the judgement of a cash manager or credit analyst, it can act as a valuable starting point—a building block that enhances the efficiency and breadth of analysis.

AI has particular strength as an audit and control tool. It enables the testing of data and models in ways that would be difficult to replicate manually, helping to identify inconsistencies, edge cases, or structural weaknesses in processes.

In addition to data aggregation, AI can also serve as a valuable tool for validating and checking the work carried out by humans. For example, AI-driven systems can be used to cross check calculations, confirm adherence to investment guidelines or regulatory standards, or identify discrepancies in cash flow projections. Beyond this, AI has particular strength as an audit and control tool. It enables the testing of data and models in ways that would be difficult to replicate manually, helping to identify inconsistencies, edge cases, or structural weaknesses in processes. AI can also be used to quickly build independent models to reconcile and validate data, particularly when dealing with third-party sources. This provides an additional layer of assurance, enhancing the robustness and reliability of the overall investment and operational framework.

However, despite these benefits, AI should be approached with a degree of caution. AI systems are only as reliable as the data and models on which they are built, and they are not immune to errors. Misinterpretation of data, outdated inputs, or flawed assumptions can lead to incorrect outputs, which, if unchecked, may have material consequences. As such, human oversight remains a critical component of the process. Cash managers must continue to apply their expertise and judgement to validate results, challenge outputs, and ensure that investment decisions are made with the core objective of the fund in mind and do not solely rely on outputs generated by AI.

AI does not think like a human and it cannot form views, for example views on interest rate expectations or the broader macroeconomic outlook.

There is an important distinction here between assistance and replacement. While AI can enhance many aspects of cash management, it is unlikely to replicate the expertise of a cash manager. Decisions around risk appetite, counterparty selection, and portfolio construction require a qualitative assessment that extends beyond quantitative data. In periods of market stress, where markets can behave unpredictably, the value that a cash manager can bring to the table becomes invaluable. AI does not think like a human and it cannot form views, for example views on interest rate expectations or the broader macroeconomic outlook.

In conclusion, AI represents a powerful tool within cash management, offering meaningful improvements in efficiency, data analysis, and operational control. Its ability to process information at scale, automate repetitive tasks, and support credit analysis can add value to the investment process. However, it should be viewed as an enabler rather than a replacement. By combining the strengths of AI with the judgement and experience of cash managers, we are slowly entering a new world of opportunity. 

For professional investors only. This material is not suitable for a retail audience. Capital at risk. This is a financial promotion and is not investment advice. Past performance is not a guide to future performance. The value of investments and any income from them may go down as well as up and is not guaranteed. Investors may not get back the amount invested. Portfolio characteristics and holdings are subject to change without notice. The views expressed are those of the author at the date of publication unless otherwise indicated, which are subject to change, and is not investment advice.