Agentic AI in ALM/Treasury: The Future of Liquidity Risk Management

Liquidity risk management has typically treated speed, control, and explainability as a trade-off: fast means “approximate,” controlled means “slow,” explainable means “post-mortem.” AI is raising expectations and forcing a new operational regime: answers in minutes, not days, with lineage you can defend.

The winning storyline for a treasurer, liquidity manager, or CFO is not, “we deployed AI.” It’s, “we upgraded decision velocity while upgrading control.” Yet, most liquidity frameworks are still batch, assumption-heavy, and backward-looking, operating as a periodic measurement exercise. Liquidity risk management doesn’t fail because people can’t compute ratios. It fails because time-to-trust is too long. The bottleneck is classically a recurring reconciliation between treasury, risk, finance, and front-office sources before the analysis even starts. The numbers are produced but explanations are not fast enough to bring actionable decisions on the table. The stress scenarios cover limited scope and can be hard to replay in audit terms.

Agentic AI Isn’t About “Fancy Text” - It’s About Shrinking Time-to-Trust

In this context, agentic AI is not expected to generate fancy text but more likely to contribute to shrinking the time-to-trust: it could efficiently master and manage the appropriate sequence of  tasks, e.g., detecting data quality issues, automatically drilling down the variations, and applying advanced analytic tools on a subset of portfolios, stressing a twin dataset, keeping full transparency and supervision in the suggested process.

The role of the analyst is shifting from manual analysis for understanding the variation, to approving the actions and supporting the sign-off at pace. AI becomes the operating tool to orchestrate a workflow and leverage the fragmented information. This is a crucial distinction: AI does not “solve” liquidity risk; it helps to loop quickly, access the root cause as fast as possible, and bring transparency to the decision process.

Automation is not the ultimate goal, the controlled execution readiness is. The future of agentic AI will be the creation and execution of the ultimate playbook:

1. Continuous preparation of a set of feasible actions (“Explain” agent, movements breakdown by Legal Entity, Desk, Maturity buckets, Drivers analysis) leading to Counter Measures propositions
2. Prioritization of these suggested actions by liquidity impact, cost, constraints, and operational readiness (e.g., buy-side example: time-to-cash plans that reflect market impact under stress)
3. Summary and routing them through approval workflows

The CFO's reaction will move tomorrow from, “Are we comfortable with the baseline? What has caused the current situation? How do we solve issues?” to a more execution-centered posture, “What are our levers? How fast can we pull them? What could go wrong if we do?”

Despite different instruments and balance-sheet structure, both sell-side and buy-side will be sharing the future state with a decisionable and streamlined liquidity process. They will rely on a liquidity tower, continuously updated and controlled by agentic capabilities:

Orchestrator
Liquidity processes are multi-step and cross-functional: data sourcing, normalization, reconciliation, aggregation, limit checks, scenario refreshes, reporting, and escalation. An orchestrator agent coordinates these steps, enforces sequencing, and ensures every output is linked to inputs and controls. The prize is not novelty - it’s consistency at speed.

Explain
Variations become detailed, drilled down, and sensitivity drivers highlighted and classified. The complexity of the calculation chain is no longer an issue as long as you can differentiate between referential evolution, unexpected taxonomy, business rules modification, or material gap raise. The painful analysis process to bring the evidence will shift to understand and anticipate the corrective actions.

Stress Scenario Factory
Traditional stress testing is expensive: heavy setup, limited permutations, slow refresh cycles. Agentic AI changes that by making scenario generation and parameterization far less manual. You move from “a few approved scenarios periodically” to a broader library refreshed more often, with scenario templates, driver-based shocks, and documented rationale that will ensure a broader coverage of risk.

Early Warning
Early warning is not just about predicting a liquidity shortfall. It is about detecting drift that is not always reflected into the regulatory metrics - the subtle shifts that precede operational stress. AI can industrialize that intuition by detecting patterns such as:

• acceleration in margin calls (frequency and magnitude)
• clustering of settlement fails (by counterparty, venue, instrument type)
• funding capacity erosion (haircut shifts, tenor shortening, spread jumps)
• client drawdown behavior and concentration changes
• collateral mobility constraints emerging (eligibility changes, concentration limits, operational cutoffs)

The value is time: earlier escalation windows, more structured intervention, and cheaper fixes.

That being said, financial institutions might struggle to transform their strategic vision into a credible plan. There is not a single meeting without hearing from the CFO or Treasurer:

“Where do I start? I have a multi-year program going to refurbish the legacy system and operating model. Should I wait for them to deliver before introducing more complexity? My data are very fragmented, and I have more issues than certainties. I am not ready for intangible innovation that will consume my limited resources..."

These concerns are rational - but they can no longer justify inaction. The technology wave is arriving either way, and it will ultimately reward the institutions that turn it into practical operational excellence. The key will be efficiency and bringing a staged approach starting small and scaling along the journey.

A Practical Staged Approach: Start Small, Prove Control, Then Scale

Don’t start with a chatbot. Don’t start with a grand AI model. Start with the spine: a minimal, production-grade data and controls layer that can answer four questions every day, with timestamps and ownership. The confidence being created will be the backbone of the next step acceleration.

Then:

• test and refine your first playbooks using decision templates that are “approval-ready”
• run them on a sub-perimeter
• prove accuracy and cycle-time reduction before expanding scope
• redeploy the operational bandwidth you free up (or strengthen buffer policy and governance with better evidence)

Once the foundation is stable, you can scale into wider adversarial stress coverage, bring the liquidity twin live for continuous simulation, and increase scenario refresh frequency without multiplying manual workload.

Bringing AI to life should be pragmatic following these three principles:

1. Keep supervision and business autonomy on top, over “autopilot”
   The goal is not to remove humans from the loop. The business should be supported with a system that prepares decisions faster, with clearer evidence, so they can supervise, approve, and escalate with confidence. AI should increase accountability - not dilute it.
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2. Explainability as a first-class operating requirement
   Explainability cannot be an afterthought attached to the end of the process. It must be engineered into the workflow: lineage, reconciliations, transformation logs, and replayable decisions. When the evidence is continuously assembled, the organization stops treating explainability as “post-mortem work” and starts treating it as decision infrastructure.
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3. Progressive delivery with measurable levers
   Bring access to the data, enforce data health controls, deploy a few high-value playbooks, standardize obvious systemic actions leading to certification, and measure cycle time. Then scale and bring more complexity into the scheme.

The future of liquidity risk management is not “more intelligence.” It is controlled, explainable speed - and a staged path to get there without waiting for perfect systems, perfect data, or perfect conditions.

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