Artificial Intelligence in Corporate Treasury:

Corporate treasury stands at the threshold of a new era: artificial intelligence (AI) brings precise liquidity planning, real-time risk management, and well-founded decisions. The KPMG study "Generative AI in the German Economy 2025¹" finds that AI is a key prerequisite for competitiveness, innovation and efficiency. Waiting is not an option – the gap is widening between companies that successfully deploy AI and those that stay on the sidelines.

Three forces are driving this shift in Treasury: rising market complexity, more stringent regulation and pressure to automate processes while boosting efficiency and employee satisfaction. Those who act now can elevate the treasury function from a pure process handler to a strategic control center.

Volatile Markets and Complex Environments
Global crises, geopolitical tensions and dynamic interest rate developments are creating major uncertainties around currencies, supply chains and financing costs. At the same time, ESG requirements are also gaining traction and must be integrated into financial and liquidity planning.²

Treasurers therefore need agile tools that link internal and external data for swift and well-founded decisions. Internal data such as ERP information, bank accounts and cash flow histories are combined with external factors like market trends, currency risks, supply chain information or ESG requirements. AI-driven models then generate forecasts and clear action plans.³

Example: A corporate treasury team must provide liquidity at short notice because a planned payment to a supplier is delayed due to supply chain disruptions. AI could analyze internal cash flows and open invoices as well as external exchange rate risks to recommend which payments to prioritize, whether short-term FX hedges are needed, or if credit-line funding makes sense. This lets Treasury react quickly and precisely without having to conduct time-intensive manual analyses.

Rising Regulatory Pressure
New regulations are raising the bar on transparency, traceability and risk management – even for corporate treasury. DORA⁴ (Digital Operational Resilience Act) requires that IT risks in treasury systems, bank connections, and payment processes be comprehensively monitored, documented and tested. Basel IV indirectly affects corporates, as more precise risk assessments at banks impacts credit conditions, collateral requirements and financing costs.

Treasury must now manage liquidity, currency, and interest-rate risks with greater precision while staying compliant. AI can help by automating workflows, continuously monitoring risk, and powering robust scenario analyses. The result? A treasury function that’s both compliant and strategically empowered.

Efficiency, Employee Relief and Innovation Edge
KPMG’s “Generative AI in German Business 2025⁵” shows that 63% of companies rank efficiency gains as AI’s top benefit, 45% expect higher employee satisfaction, and 69% foresee an innovation lead. In Treasury, this is particularly evident in recurring tasks such as cash flow reconciliation, account reconciliation or reporting. AI automates these processes, reduces errors and frees teams for strategic analyses, risk management and proactive management consulting.

A central driver for AI adoption in treasury is internal pressure to improve efficiency, control costs, and enhance employee morale. According to the same KPMG study, 63% of companies see efficiency gains as the biggest upside. For treasury teams, that means automating recurring tasks like cash flow reconciliation, account reconciliation or standard reporting. This reduces error rates and creates bandwidth for value-adding activities – from scenario management to proactive consulting of executives.

Employees benefit, too: 45% of companies expect AI to boost job satisfaction. In Treasury, that translates to less firefighting, smoother month-end routines, and relief in day-to-day operations. AI-supported assistants make complex analyses more accessible, promote new competencies in handling data, and thus enhance the overall work environment.

Moreover, 69% of companies expect AI to give them an innovation edge. Treasury can take on new roles: Instead of static reports, dynamic dashboards with scenario simulations emerge that display interest or FX developments in real-time. Risks are not only detected faster but actively managed. New opportunities also open up in working capital – AI predicts customer payment behavior and enables targeted tailored payment terms. Early pilot projects – AI-driven hedging and automated forecasts – show how these innovations can accelerate Treasury’s innovation cycle.⁶

After outlining the transformation drivers – volatile markets, tighter regulation, and the push for efficiency, employee satisfaction, and innovation – it becomes clear why AI is indispensable in Treasury. Its biggest benefits show up in three key areas: liquidity planning, FX risk management, and fraud prevention/receivables management.⁷

Predictive Liquidity Planning: From Rearview Insights to Real-Time Control
Traditional cash-flow forecasts are often too slow and imprecise for today’s turbulent markets. AI models can bridge that gap. Advanced algorithms like Long Short-Term Memory (LSTM)⁸ neural networks analyze historical cash-flow data across time, spot hidden patterns, and weave together internal sources – ERP systems, bank balances – and external inputs such as market trends and supply-chain developments. The outcome? Highly accurate forecasts that empower treasurers to make decisions instantly and expand their strategic playbook. For example, Bosch trimmed its cash buffer by 30 percent⁹.

Intelligent FX Risk Management: AI as Hedging Co-Pilot
Manual hedging decisions often lag behind market movements. Treasury teams must balance a flood of data, from cash-flow projections to FX rates to geopolitical alerts, which can slow down execution. Cognitive biases like overcaution or following the crowd further cloud judgment.

AI tackles these challenges by: Ingesting and interpreting live market data, detecting emerging trends and quantifying risks impartially and then recommending the optimal moments to hedge. With reinforcement-learning algorithms, AI can even automate hedging trades, letting Treasury act faster, more efficiently and with data-backed assurance.

Fraud Prevention & Receivables Management: AI as Watchdog
Fraud and late payments, especially on receivables, place a heavy burden on Treasury and the wider business. AI steps in to detect suspicious behavior, monitor transaction flows and communications in real time, and forecast which customers are likely to pay late.¹⁰

Successful AI deployment in treasury demands a structured approach, clear priorities, and proven practices. Companies that adopt AI effectively pair strategic planning with hands-on execution, spanning data foundations through full-scale rollout.¹¹

Implementing AI in treasury goes beyond individual tools – it’s a continuous, strategy-driven transformation. Treasury teams must not only pinpoint specific use cases but also craft an overarching data strategy that centralizes internal systems, bank connections and external information feeds. Only then can they establish reliable bases for automation, risk analytics and reporting.

AI implementation can be organized into five sequential phases: 

1. Strategy: Begin by identifying your top treasury use cases, whether that’s cutting Days Sales Outstanding or refining cash-flow forecasts. Define clear KPIs and build a centralized data architecture. Early on, appoint AI champions to drive adoption, train stakeholders and ensure practical deployment of solutions.
2. Data: A robust data infrastructure is the cornerstone of trustworthy analytics. Integrate ERP systems and bank accounts, clean and structure your datasets and incorporate external inputs like market data, currency trends and ESG risk indicators. That's the only way to get a solid basis for making decisions. Cloud platforms add scalability and flexibility, rounding out a future-proof foundation.
3. Change Management: Boost team buy-in with targeted training and quick-win initiatives. AI champions support end users, share best practices, and cultivate prompting skills¹², all of which are necessary for efficient collaboration with AI.
4. Pilot Phase: Test selected use cases at small scale – think AI-driven reporting or predictive cash-flow analysis. Embrace a “fail fast, learn fast” mindset to fine-tune models quickly and collect valuable feedback before scaling across the entire treasury function.
5. Scaling: Roll out governance policies, keep models in continuous training and enable real-time data processing. Track long-term KPIs like crisis resilience to ensure sustainable value. Choose technologies that remain scalable, adaptable, and compliant with data-privacy standards.

Best Practices from the Field
In addition to the roadmap, several best practices have proven successful in leading companies:

• Robust Data Infrastructure: Centralize and structure internal (ERP, bank accounts, cash-flow) and external (market data, FX rates, supply-chain, ESG) sources to boost forecast accuracy and foster cross-functional collaboration between Treasury, Controlling and Risk Management.
• Multidisciplinary AI Team: Bring together experts from Treasury, Data Science, Compliance and Business Analytics. The result? The AI solutions target the right problems, remain practical and meet regulatory requirements.
• Targeted Technology Selection: Companies need to decide whether current tools are enough or if a custom platform is necessary. The key factors are scalability, adaptability, interoperability and data privacy. Careful tech choices help avoid fragmented solutions, ensuring longevity.
• Strict Governance: Clear guidelines for data accessibility, result management, model verification, and staff training are essential to minimize compliance risks. Governance ensures that AI systems stay accurate, transparent, and trustworthy.
• Pilot Projects: Start with small-scale, low-risk initiatives like AI-driven monthly reports or forecasts. These projects foster support, provide valuable feedback for refining models and set the stage for broader deployment.

By combining a structured roadmap with proven best practices, AI is not just implemented in Treasury, but becomes strategically impactful by boosting efficiency, reducing risk and driving innovation.

AI is becoming a strategic ally in treasury, controlling real-time liquidity planning, optimizing intraday FX risk management and generating dynamic reports. It also automates fraud detection and receivables management, providing consistent, reliable data. Centralized data repositories¹³ and cloud solutions ensure agility, flexibility, and transparency.

This is why the underlying technological infrastructure¹⁴ is becoming ever more crucial: Consolidated data repositories create a holistic view of balance sheets, capital and liquidity, while cloud technologies make scalable analyses possible without major hardware investments. Modular systems and open interfaces mean new AI features can be added in a flexible way.

Treasury skills are also evolving: Treasurers are moving from being pure process handlers to strategic analysts who specifically control AI. Prompting skills are gaining in significance and will be a key success factor.

Data sovereignty is equally important: It is incumbent upon treasury teams to ensure that data quality, access, and usage comply with regulatory requirements at all times while also delivering maximum control benefits. Doing so requires a deep understanding of data architectures, governance and the integration of external sources such as market data or ESG metrics.

What does this mean for companies? They absolutely need to invest in central data architectures, launch targeted pilot projects and promote new skills profiles. Having agile control processes that enable swift decisions is just as essential as continuously developing AI models (prompting).

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

• AI is indispensable: Companies using AI early and successfully secure clear competitive advantages – from cost savings of up to 30% to forecasts that are up to 90% more accurate¹⁵.
• The "how" of implementation is the key to success: You will want to have high-quality data, pilot projects with quantifiable added value and consistent change management that pulls in the entire organization.
• The future belongs to the pioneers: Before long, AI will transform Treasury from an operational service provider all the way to a strategic co-pilot – and those who act now will play an active role in shaping this development.

Those who act early will not only gain an edge when it comes to efficiency and innovation, but will also strengthen the position of Treasury as the central control center for the entire company.

Source: KPMG Corporate Treasury News, Edition 158, September 2025
Authors:
Nils Bothe, Partner, Finance and Treasury Management, Corporate Treasury Advisory, KPMG AG
Nils Bentzien, Manager, Finance and Treasury Man-agement, Corporate Treasury Advisory, KPMG AG

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¹ KPMG: Studie Generative KI in der deutschen Wirtschaft 2025, URL: https://kpmg.com/de/de/home/themen/2025/04/studie-generative-ki-in-der-deutschen-wirtschaft-2025.html 
² esgnew.com: IBM is launching AI-powered solutions for enhanced asset management and emissions tracking – ESG News. [Article in German]
³ TraidingView: Citi and Ant International are piloting an AI-powered FX tool to help clients cut hedging costs – TradingView News. [Article in German]
⁴ see: eur-lex.europa.eu: Regulation - 2022/2554 - DE - DORA - EUR-Lex; BaFin.de: DORA - Digital Operational Resilience Act
⁵ KPMG: Studie Generative KI in der deutschen Wirtschaft 2025, URL: https://kpmg.com/de/de/home/themen/2025/04/studie-generative-ki-in-der-deutschen-wirtschaft-2025.html 
⁶ DerTreasurer: What Artificial Intelligence Delivers for Hedging [Article in German]
⁷ McKensey: AI in the workplace: A report for 2025 | McKinsey
⁸ LSTM networks are specialized neural nets built to process time-series data, perfect for multi-month cash-flow forecasting.
⁹ Reitzenstein, B., Pinzger, A., & Pottmeyer, T. (2020). Case Study: Robert Bosch GmbH optimized net working capital using prescriptive analytics. Controlling, 32(1), 50–57. https://doi.org/10.15358/0935-0381-2020-1-50
¹⁰ acfe.com: Global Fraud Survey
¹¹ Wildhirt, K., Bub, U. & Vogel, M. Generative KI erfolgreich in Unternehmen implementieren. Wirtsch Inform Manag (2025); 
Schäffer, Utz. “Generative AI becomes reality faster than any other hype.” In.: Controlling & Management Review, Vol. 69, 32–37 (2025); 
Breiter, K., Lohmann, T., Stahl, B. et al. Generative KI in der Finanzbranche: Strategic, technological, and organizational implementation using DZ BANK AG as an example. HMD (2025)
¹² Prompting: the art of crafting precise questions or instructions for AI systems to deliver accurate results.
¹³ Centralized data repositories refer to the consolidated and structured storage and structuring of all relevant Treasury and financial data for AI analysis.
¹⁴ KPMG: Treasury Management 2030:built on generative AI and new infrastructure
¹⁵ KPMG: Study “Generative KI in der deutschen Wirtschaft 2025” [Generative AI in the German economy], URL: https://kpmg.com/de/de/home/themen/2025/04/studie-generative-ki-in-der-deutschen-wirtschaft-2025.html