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Alexander Odenthal, Herwig Temmerman, Hector Dumas (BearingPoint): Harnessing the Power of AI in Liquidity Planning
If cash is king, then efficient and precise liquidity planning is critical. Alexander Odenthal, Herwig Temmerman, partners, and Hector Dumas, business analyst at BearingPoint, say that artificial intelligence (AI) promises to fundamentally improve our productivity and forecasting capabilities.
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Before exploring the opportunities, risks and prerequisites of using AI in liquidity planning, can you remind us of what liquidity planning is all about?
The purpose of liquidity planning is to ensure that an organization has enough cash to meet its short-term obligations and to optimise the use of cash to maximise returns on surplus funds while minimising costs. To achieve this cash flow, forecasting is key, and a funding strategy should be established. Liquidity levels must be tracked continuously and financial scenarios for potential liquidity stress situations must be prepared.
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What are the top challenges for liquidity planning?
Many of the previously described tasks are still done manually. This makes it very time-consuming and labour-intensive to collect, input and analyse the data. The process is slow, prone to human error, and lacks the ability to integrate real-time data, resulting in delayed or outdated insights that hinder effective decision-making. Data often resides in disparate spreadsheets across departments, leading to inconsistent data and a fragmented view of overall liquidity.
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How can AI play a role in overcoming these challenges for liquidity planning?
By means of machine learning (learning from data), deep learning (identifying abstract hierarchies of features) and generative AI (producing new data based on detected patterns and information), AI can help to derive deep insights and drive data-driven decisions for liquidity planning. It can automate routine planning tasks, streamline processes, and speed up analysis and reporting. Treasurers will be able to enhance their forecasting with improved accuracy, real-time adjustments and predictive insights. Risk will be better mitigated. ​​
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“AI will enhance the accuracy of cash flow predictions and will increasingly enable real-time analysis and decision-making, automating liquidity planning.”
Herwig Temmerman
What are the prerequisites for a successful AI integration? Are there any risks?
It is important to understand how AI makes its predictions. Data quality issues may lead to flawed models, so high-quality, structured data is essential. You need adequate IT systems, software tools, and skilled personnel to manage AI systems and interpret the results. Protection against cybersecurity risks is crucial, as are fostering a culture that embraces technology and innovation and putting in place change management. Expectations must be managed – AI is not a solution for everything.
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What are the best practices for implementation?
Identifying relevant data is fundamental. The data must be extracted and standardised. It is important to prepare data for training: import standardised data into the AI system, then prepare and structure the data, taking various scenarios into account. And most crucially, don’t become obsessed with the big wins – build on small successes.
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