Artificial intelligence in corporate treasury

Corporate treasury is on the threshold of a new era: artificial intelligence (AI) enables precise liquidity planning, real-time risk management and well-founded decisions. The KPMG study "Generative AI in the German economy 2025¹" shows that AI is a key prerequisite for competitiveness, innovation and efficiency. Waiting is not an option - the gap between companies that are successfully using AI and those that are reluctant to do so is widening.

Three developments are driving this change in treasury in particular: increasing market complexity, stricter regulation and the pressure to automate processes and increase efficiency and employee satisfaction. Those who act now can develop the treasury function from a pure process handler to a strategic control centre.

Volatile markets and complex framework conditions

Global crises, geopolitical tensions and dynamic interest rate developments are causing considerable uncertainty with regard to currencies, supply chains and financing costs. At the same time, ESG requirements are becoming increasingly important and need to be integrated into financial and liquidity planning.²

Treasurers therefore need flexible tools that link internal and external data in order to make quick and well-founded decisions. Internal data such as ERP information, bank accounts and cash flow histories are combined with external factors such as market developments, currency risks, supply chain information or ESG requirements. AI-supported models can use this data to derive forecasts and make recommendations for action.³

Example: A corporate treasury team needs to provide liquidity at short notice because a planned payment to a supplier is delayed due to supply chain disruptions. AI could analyse internal cash flows and outstanding invoices as well as external exchange rate risks and recommend to the treasurer which payments should be prioritised, whether short-term FX hedges are necessary or liquidity should be secured via credit lines. This would allow treasury to react quickly and precisely without having to carry out time-consuming manual analyses.

Increasing regulatory pressure

New regulations are increasing the requirements for transparency, traceability and risk management - including for corporate treasury. DORA⁴ (Digital Operational Resilience Act) requires IT risks in treasury systems, bank connections and payment processes to be fully monitored, documented and tested. Basel IV has an indirect impact on corporates, as more precise risk assessments at banks influence credit conditions, collateral requirements and financing costs.

Corporate treasury must therefore manage liquidity, currency and interest rate risks even more precisely and fulfil compliance requirements at the same time. AI can provide support here by automating processes, continuously monitoring risks and enabling well-founded scenario analyses. This not only makes the treasury function compliant, but also gives it more strategic room for manoeuvre.

Efficiency, employee relief and an innovative edge

According to the KPMG study "Generative AI in the German economy 2025⁵", 63% of companies see increased efficiency through AI as the greatest benefit, 45% expect greater employee satisfaction and 69% assume that AI will give them an innovative edge. In treasury, this is particularly evident in recurring tasks such as cash flow reconciliations, account reconciliations and reporting. AI automates these processes, reduces errors and creates scope for strategic analyses, risk management and active management consulting.

A key driver for the use of AI in treasury is the internal pressure on efficiency, costs and employee satisfaction. According to the KPMG study "Generative AI in the German economy 2025", 63% of companies see increased efficiency as the greatest benefit. In treasury, this means automating recurring tasks such as cash flow reconciliations, account reconciliations and standard reporting. This reduces error rates and frees up time for value-adding activities - from scenario management to actively advising management.

Employees also benefit: 45% of companies expect greater satisfaction through the use of AI. In treasury, this is specifically reflected in a lower workload due to ad hoc crisis measures, better planning of month-end activities and a noticeable reduction in day-to-day business. AI-supported assistance systems make complex analyses more accessible, promote new skills in dealing with data and thus increase the attractiveness of the working environment.

In addition, 69% of companies believe that AI will give them an innovative edge. Treasury can take on new roles as a result: Instead of static reports, dynamic dashboards are created with scenario simulations that map interest rate or FX developments in real time. Risks are not only recognised more quickly, but also actively managed. New scope is also opening up in working capital - AI predicts the payment behaviour of customers and enables the targeted adjustment of payment terms. Initial pilot projects such as AI-supported hedging or automated forecasts show how innovation cycles can be significantly accelerated.⁶

Having outlined some of the drivers of transformation - volatile markets, increasing regulation and the pressure on efficiency, employee satisfaction and innovation - it is clear in practice why AI is indispensable in treasury: Its greatest benefits unfold in three key application areas - liquidity planning, FX risk management and fraud prevention/receivables management.⁷

Predictive liquidity planning: from rear-view mirror to real-time control

Traditional cash flow forecasts are often inaccurate and react too slowly to volatile markets. AI models can provide much better support here. Special algorithms such as LSTM networks ("Long Short-Term Memory")⁸ analyse historical cash flow data over time, recognise patterns and take into account complex correlations between internal data (such as ERP, bank accounts) and external factors such as market developments or supply chain information. This results in highly accurate forecasts that support treasurers in real time and reveal room for manoeuvre. Bosch, for example, reduced its cash buffer by 30 per cent⁹.

Intelligent FX risk management: AI as a hedging co-pilot

Manual hedging decisions are often too slow. Treasury teams have to consider numerous internal and external data sources simultaneously - from cash flow forecasts to market and currency information to geopolitical developments. This takes time and means that decisions are often delayed. In addition, uncertainty and cognitive biases such as excessive caution or orientation towards other market participants can impair the quality of decisions.

AI can address these challenges: It analyses real-time market data, recognises patterns, assesses risks objectively and recommends optimal hedging times. Through reinforcement learning algorithms, transactions can be executed automatically, allowing a treasury to act faster, more efficiently and based on data.

Fraud prevention & receivables management: AI as a watchdog

Fraud and late payments, especially incoming payments, place a considerable burden on treasury and companies. AI recognises suspicious patterns, analyses payment flows and communication in real time and predicts the payment behaviour of customers.¹⁰

The successful use of AI in treasury requires a structured approach, clear priorities and tried-and-tested practices. Companies that introduce AI efficiently combine strategic planning with pragmatic action - from the database to scaling.¹¹

The successful use of AI in treasury requires more than individual tools - it is a continuous, strategically managed transformation. Treasury organisations must not only identify specific use cases, but also develop an overarching data strategy that brings together internal systems, bank connections and external information sources centrally. This is the only way to create a valid decision-making basis for automation, risk analyses and reporting.

The introduction of AI can be divided into five successive phases:

Strategy: First, the most important treasury use cases are identified, such as the reduction of DSO or the optimisation of cash flow forecasts. Clear KPIs are defined and a centralised data architecture is set up. AI champions should already be appointed in this phase to act as internal drivers during implementation, train teams and ensure that the AI solutions are used in a practical manner.

Data: A robust data infrastructure forms the basis for reliable analyses. ERP systems and bank accounts are integrated, data is cleansed and structured. External information, such as market data, currency developments or ESG risks, is incorporated. This is the only way to create a valid basis for decision-making. Cloud solutions also ensure scalability and flexibility.

Change management: To increase acceptance within the team, training is provided and quick wins are implemented. AI champions support users, share best practices and promote the development of prompting skills¹² that are necessary for efficient collaboration with AI.

Pilot phase: Use cases are tested on a small scale, for example AI-supported reporting or predictive cash flow analyses. The "fail fast, learn fast" approach allows models to be optimised quickly and valuable feedback to be collected before the solutions are scaled across the entire treasury.

Scaling: Governance rules are implemented, models are continuously trained and real-time processing is introduced. Long-term KPIs such as crisis resilience ensure sustainable success. The technology is selected specifically so that it remains scalable, customisable and compliant with data protection regulations.

Best practices from the field

In addition to the roadmap, some best practices have proven themselves in leading companies:

Robust data infrastructure: a centralised and structured database that links internal systems (ERP, bank accounts, cash flow data) and external sources (market data, currency information, supply chain and ESG data) increases the accuracy of forecasts and analyses. At the same time, it facilitates cross-departmental collaboration between Treasury, Controlling and Risk Management.

Multidisciplinary AI team: Successful treasury AI projects benefit from teams that bring together experts from treasury, data science, compliance and business analysis. This ensures that AI solutions address the right problems, remain practical and fulfil regulatory requirements.

Targeted technology selection: Companies must decide whether existing tools are sufficient for their requirements or whether a customised internal platform is necessary. The criteria here are scalability, customisability, integration capability and data protection. A conscious technology decision prevents isolated solutions and ensures sustainable utilisation.

Strict governance: Clear rules for data access, results monitoring, model validation and employee training reduce legal and compliance risks. Governance ensures that AI solutions remain trustworthy, traceable and audit-proof.

Pilot projects: Start with low-risk, measurable use cases, such as AI-supported monthly reporting or forecasting. Pilot projects create acceptance, provide valuable feedback for adapting the models and form the basis for the successful, scaled introduction throughout the entire treasury department.

By combining a structured roadmap and best practices, AI is not only implemented in treasury, but becomes strategically effective, increases efficiency, reduces risks and promotes innovation.

AI is increasingly becoming a strategic co-pilot in treasury. For example, it makes liquidity planning controllable in real time, enables intraday optimisation in FX risk management and provides dynamic reporting. At the same time, fraud prevention and receivables management are automated and continuously provide reliable data. Centralised data repositories¹³, cloud solutions and modular interfaces ensure that the treasury can act faster, more flexibly and more transparently.

The technological infrastructure¹⁴ is becoming increasingly central to this: consolidated data warehouses create a comprehensive view of the balance sheet, capital and liquidity, while cloud technologies enable scalable analyses without major hardware costs. Modular systems and open interfaces also allow flexible integration of new AI functions.

The competences in treasury are also changing: treasurers are moving from pure process handlers to strategic analysts who manage AI in a targeted manner. Prompting skills are gaining in importance and are becoming a key success factor.

Data sovereignty is just as important: treasury teams must ensure that data quality, access and utilisation comply with regulatory requirements at all times while simultaneously maximising the management benefits. This requires a deep understanding of data architectures, governance and the integration of external sources such as market data or ESG key figures.

For companies, this means Investment in centralised data architectures, targeted pilot projects and new skills profiles are crucial. Agile management processes that enable quick decisions are just as essential as the continuous further development of AI models (prompting).

AI has long been more than a one-off project - it is a continuous transformation that makes treasury more efficient, manages risks better and supports strategic decisions in real time. Three key findings show what is important now:

• AI is essential: companies that implement AI early and successfully secure clear competitive advantages - from up to 30% cost savings to 90% more accurate forecasts .
• Success lies in implementation: high-quality data, pilot projects with measurable added value and consistent change management that takes the organisation with it are crucial.
• The future belongs to the pioneers: In the near future, AI will transform treasury from an operational service provider to a strategic co-pilot - those who act now will actively shape this development.

Those who act early will not only gain efficiency and innovation advantages, but will also strengthen the position of treasury as a central control centre for the entire company.