Summarise the article with your AI
Here you can read:
According to OECD data, AI adoption by firms has more than doubled over the past two years, with approximately 52% of large firms now using AI across OECD countries. In finance, a survey of over 100 financial executives and CFOs at medium and large companies in Europe, show that 53% of organisations now view AI implementation as the most significant technology trend on treasury management. At the same time, 29% cite data security and privacy as their top concern, while 27% admit to lacking internal knowledge or experience with AI.
The potential is significant. The path forward, however, remains uncertain for many.
Ask a treasury and finance professional what takes up their time, and the answer is often predictable: bank reconciliation, payment management, cash flow forecasting, manual data entry, and endless spreadsheet gymnastics. It's the operational grind that keeps the lights on but rarely moves the strategic needle.
A typical treasury analyst may spend several hours a week generating forecast comparison reports. That's time not spent on scenario planning, capital allocation decisions, or strategic risk management.
This is where artificial intelligence enters: not as a replacement, but as a fundamental shift in how work gets done. According to Embat's survey data, 39% of finance executives identify the automation of bank reconciliation and payment management as the area where AI can have the strongest impact. Another 17% highlight fraud and anomaly detection, while 16% highlight reporting and liquidity data analysis.
A pattern is emerging. AI's early applications in treasury often target the high-frequency, high-pain tasks that drain time and introduce error. Consider an AI assistant powered which has been trained with over 10 million transactions. The system doesn't wait for month-end to reconcile accounts. It continuously processes bank transactions, identifies patterns in payment behaviour, automatically categorises entries, and flags discrepancies in real time. For companies managing multiple entities, currencies, and banking relationships, this can help shift treasury towards a more proactive risk management approach.
The impact? Time savings up to 10 hours per week on accounting tasks alone, with automation rates that can exceed 90% for routine bank reconciliation and payment accounting. For many organisations, this represents meaningful operational improvement.
The concerns: what keeps CFOs awake at night
Yet enthusiasm alone doesn't build trust. And trust, particularly in finance, is earned through transparency, control, and demonstrated reliability over time.
Embat's survey data reveals a significant gap between interest and confidence. When asked about concerns regarding AI, 35% of respondents specifically cited security and ethical use of sensitive data as their top worry. Another 24% highlighted the need to quickly acquire new skills, while 25% expressed concern about possible staff cutbacks due to automation.
These aren't irrational fears. Recent reports note that AI may amplify certain financial sector vulnerabilities and pose risks to financial stability, with several AI-related vulnerabilities standing out including third-party dependencies and service provider concentration, market correlations, cyber risks, and model risk.
Regulators have responded with increased attention. Financial authorities have issued guidance emphasising model risk management, data governance, explainability, and the need for human oversight.
For CFOs, this creates a dual mandate: innovate to remain competitive, but ensure every AI deployment is auditable, explainable, and compliant. Security, in particular, demands attention. Financial institutions handling sensitive customer data, payment information, and proprietary treasury positions cannot afford breaches.
This is why many treasury AI platforms now operate within ISO 27001-certified environments, use end-to-end encryption, and ensure that customer data isn't used to train third-party foundation models without explicit consent.
From experiment to execution
So where does this leave the treasury function? More companies may follow the lead of AI front-runners by adopting an enterprise-wide strategy centred on a top-down programme, where senior leadership identifies key workflows or business processes where payoffs from AI can be substantial.
For treasury teams, this could mean starting with clarity about where AI can deliver measurable value. The data suggests three potential priority areas:
First, automate the operational core. Bank reconciliation, payment processing, and transaction categorisation are high-volume, rules-based processes where AI can deliver relatively rapid ROI. These may be the foundational use cases that build organisational confidence and free up capacity for more complex applications.
Second, enhance forecasting and risk management. AI-driven cash flow forecasting, payment prediction, and liquidity modelling can address forecast accuracy gaps by processing real-time data, identifying pattern shifts, and providing scenario analysis that human teams may find difficult to match at scale.
Third, build a culture of AI-augmented decision-making. Some treasury professionals worry that AI will eliminate their roles, but early evidence suggests that AI often eliminates tasks, not expertise. The treasurers who may thrive in an AI-augmented landscape are likely to be those who can interpret AI insights and challenge assumptions.
The emerging role of algorithms in treasury may not be one where machines make all the decisions. It could be one where machines surface the insights, flag the risks, and execute the routine tasks, while humans apply judgement, manage relationships, and drive strategy.
The verdict: AI as your treasury assistant
Two years ago, AI in treasury was a conference buzzword. Today, it is increasingly viewed as a competitive consideration. Many firms moving quickly aren't waiting for perfect clarity or comprehensive frameworks. They're starting with focused use cases, embedding governance from day one, and building organisational muscle around AI-augmented work.
Embat's survey data tells part of the story:
| Finding | Percentage |
| Organisations prioritising AI implementation | 67% |
| Time savings cited as primary benefit | 50% |
| Bank reconciliation identified as highest-impact use case | 39% |
| Cybersecurity and fraud prevention seen as critical trend | 36% |
| Data security cited as top concern | 29% |
Source: Embat CFOs Survey
Treasury managers are not worried the algorithm will replace them. They're grateful it will give them a head start and time savings. Because in treasury, as in most of finance, the value isn't in the data. It's in what you do with it.
Methodology Note:
This article draws on Embat's proprietary survey of more than 100 financial executives and CFOs at medium and large companies primarily based or operating in Europe, as well as publicly available research from the OECD, Financial Stability Board, and the Association of Corporate Treasurers. For more on how AI is transforming finance departments, see Embat's insights on the automation of the finance department.
