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🛟 How AI Is Quietly Taking Over Treasury Decisions
AI-powered treasury platforms for banking and corporates are accelerating cashflow, unlocking capital, and surfacing new product opportunities. It works brilliantly until the assumptions break. When the next liquidity crunch hits or the model quietly drifts, will solvency still hold?
Abbie Widin
May 20, 2025
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The AI Check In is your weekly power play to navigate AI governance and strategy in banking and finance.
What to expect this edition:
🛟 Need to Know: AI Governance & Risk in Bank Treasury
🥷 Deep Dive: AI-Driven Treasury Management – How CFOs Are Automating Capital Decisions
⚔️ Vendor Spotlight: Trovata – Multi-Bank Treasury Automation
📈 Trends to Watch: AI Use in Liquidity, Forecasting & Risk
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🛟 Need to Know: AI Governance & Risk in Bank Treasury
Treasury oversight remains an afterthought in most boardrooms. However, AI systems are already shaping how U.S. banks manage billions in short-term assets.
From real-time liquidity forecasts to intraday FX positioning, AI now moves cash positions, and touches the levers that determine whether a bank remains solvent during market stress.
The tools are powerful. Currently, the governance is less than fair to middling.
Few boards ask whether these AI models can be explained, challenged, or even reconstructed when performance degrades. Fewer still demand transparency from vendors embedding generative models into core treasury workflows. Meanwhile, banks are using the same AI playbooks for credit, fraud, and capital planning without applying credit-model governance standards to treasury operations.
This is less of a compliance checkbox, it’s more a control point over how the firm allocates cash, absorbs shock, and preserves influence.
If directors want power, it starts with knowing where the real financial engines are and who’s actually running them.
🥊 Your Move:
Apply Model Governance: Treasury models must meet the same standards as credit or fraud models, including validation, documentation, and override controls.
Require Technical Audit Trails: Request retraining logs, drift monitoring, and forecast accuracy metrics.
Demand Vendor Disclosure: Ensure vendors identify AI components, every third party source, and provide access to documentation and update protocols. Include where the data is stored and know if your data is being used to help train their models for sale elsewhere.
🥷 Deep Dive: AI-Driven Treasury Management – How CFOs Are Automating Capital Decisions
U.S. banks are increasing their use of artificial intelligence in treasury operations to enhance liquidity control, forecasting accuracy, and capital allocation. AI is being applied to reconcile transactions, monitor intraday cash positions, and simulate regulatory risk buffers with a speed and scale that manual processes cannot match.