Agentic AI enables treasurers to cut reporting time by up to 70%, reduce risk-weighted assets by 5–10%, and improve cost-to-income ratios by 1–2%. It autonomously forecasts liquidity, flags compliance risks, and recommends capital reallocations—freeing teams from manual processes. As adoption grows, treasury functions shift from reactive control to proactive decision-making.
Agentic AI is fast becoming the term everyone in finance wants to be seen using—but very few are using it correctly. While chatbot assistants and copilots have taken centre stage in day-to-day operations, the next evolution is quietly reshaping how work is done at a foundational level.
For corporate treasurers navigating complex workflows—spanning liquidity management, regulatory reporting, and risk mitigation—agentic AI offers more than just efficiency. It promises autonomous, decision-capable systems that both act and reason. The implications for treasury teams? Less firefighting, more foresight.
The distinction between traditional AI assistants and agentic AI lies in autonomy. While copilots require prompts and operate within set boundaries, agentic AI solutions can break down goals, determine necessary steps, collaborate with other systems, and execute actions—without waiting for further instruction.
Think of it this way: a chatbot can tell you your cash position. An AI agent can flag liquidity risks across multiple jurisdictions, propose short-term funding strategies, and trigger approval workflows. This is delegated problem-solving.
With global volatility becoming the norm, treasury’s role as the strategic nerve centre of the organisation has intensified. But even the most seasoned treasurer can’t model market shocks, regulatory demands, and currency exposures simultaneously—at least not in real time.
Agentic AI is uniquely positioned to help. In the back half of 2025, Deloitte predicts that a significant wave of enterprise adoption will begin, particularly in functions like compliance and finance. For treasury, that means the ability to:
Run continuous, self-updating liquidity forecasts
Model currency risk across hundreds of variables
Trigger early warning systems for covenant breaches
Automate CSRD-aligned ESG reporting
These workflows are already being tested in some of the world’s largest financial institutions.
A single treasury agent is typically built using a modular approach. Core components include:
Data Ingestion Layer: Integrates feeds from ERP, TMS, bank portals, and market data providers.
Reasoning Engine: Applies financial logic to contextualise information—think IFRS 9 impairment rules or internal liquidity thresholds.
Orchestration Layer: Prioritises tasks, reroutes actions based on delays or conflicts, and ensures alignment with business policies.
Security Wrapper: Enforces access control, audit logging, and regulatory compliance.
These agents can operate individually or as part of a broader multi-agent system, collaborating with risk and compliance agents to form a dynamic virtual treasury office.
One European bank recently trialled an agentic AI tool to handle monthly impairments reporting. Rather than manually stitching together data from four systems, the AI agent observed a human analyst, learned the workflow, and replicated it across cycles.
By month three, the agent was autonomously:
Collecting external and internal datasets
Applying regulatory calculations
Flagging anomalies
Generating draft reports for review
The outcome? A 70% reduction in time spent, near-zero manual errors, and more headspace for analysis.
Perhaps the most transformative potential lies in proactive capital optimisation. Treasurers have long struggled to balance conservative capital buffers with return expectations. Agentic AI changes the game by running real-time RWA (Risk-Weighted Asset) assessments, forecasting stress scenarios, and suggesting reallocations or hedging strategies.
Notably, a reduction of even 5% in RWAs can unlock hundreds of millions in deployable capital for large institutions.
Early adopters are already seeing measurable improvements in key financial metrics:
Cost-to-Income Ratio (CIR): Agentic AI has contributed to 1–2% improvements by reducing operational costs.
Return on Equity (RoE): With capital efficiency gains, some banks project 50–100 basis point increases.
Regulatory Reporting Speed: End-to-end ESG and AML reporting workflows have been shortened from weeks to days.
Agentic AI is only as good as the environment it’s dropped into. Before implementation, treasurers must prioritise:
Clean, accessible data
A robust cybersecurity framework
Clear governance policies on decision-making
Without this, the promise of autonomy can quickly devolve into risk.
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